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The MiniMed 780G insulin pump is intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin.

The MiniMed 780G insulin pump is able to reliably and securely communicate with compatible, digitally connected devices, including automated insulin dosing software, to receive, execute, and confirm commands from these devices.

The MiniMed 780G insulin pump contains a bolus calculator that calculates an insulin dose based on user-entered data.

The MiniMed 780G insulin pump is indicated for use in individuals 7 years of age and older.

The MiniMed 780G insulin pump is intended for single patient use and requires a prescription.

The MiniMed 780G insulin pump ("780G ACE Pump") is an alternate controller enabled (ACE) pump intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin. It can reliably and securely communicate with compatible digitally connected devices, including an integrated continuous glucose monitor (iCGM), interoperable Medtronic continuous glucose monitor (CGM), and interoperable automated glycemic controller (iAGC). The pump is intended to be used both alone and in conjunction with compatible, digitally connected medical devices for the purpose of drug delivery.

The 780G insulin pump is an ambulatory, battery-operated, rate-programmable micro-infusion pump that contains pump software and houses electronics, a pumping mechanism, a user interface, and a medication reservoir within the same physical device. The pump also contains a bolus calculator that calculates an insulin dose based on user-entered data. It is comprised of several discrete external and internal components including a pump case made of a polycarbonate blend, an electronic printed circuit board assembly stacks and a drive motor system.

The 780G Pump is an interoperable device that can communicate via a Bluetooth Low Energy (BLE) wireless electronic interface with digitally connected devices. The 780G pump is a host device for the iAGC and integrates iAGC algorithm into the pump firmware. The pump is then able to receive, execute, and confirm commands from an iAGC to adjust delivery of insulin. The pump receives sensor glucose (SG) data via BLE interface from a compatible iCGM or a compatible interoperable Medtronic CGM and transmits these CGM data to the embedded iAGCs.

The 780G pump can operate in one of two modes: Manual Mode or Auto Mode (also referred to as "SmartGuard Mode"). The pump provides the user with keypad pump controls, as well as a data screen for configuring therapy settings and viewing continuous real-time glucose values, glucose trends, alerts, alarms, and other information. The user interface and alerts provide the user with the ability to interact with the pump delivery system and digitally connected devices.

The provided FDA 510(k) clearance letter and summary for the MiniMed 780G Insulin Pump (K253470) do not contain the detailed information required to fill out all requested sections of the acceptance criteria and study design. This document focuses on demonstrating substantial equivalence to a predicate device and fulfilling regulatory requirements, rather than providing a detailed clinical study report suitable for assessing device performance against specific, quantifiable acceptance criteria in the manner requested.

However, based on the provided text, I can extract and infer some information, and note where specific details are missing.

Here's an attempt to answer your request based on the provided text:

Acceptance Criteria and Device Performance for MiniMed 780G Insulin Pump

The provided FDA 510(k) summary extensively references compliance with regulatory standards and performance compared to predicate devices, particularly for "Delivery Volume Accuracy" and "Bolus Delivery Accuracy" which specify numerical criteria. Other performance aspects are described more qualitatively as meeting requirements or demonstrating safety and effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not specified in the provided document. The document refers to various "testing" and "verification activities" but does not detail the sample sizes for these tests (e.g., number of pumps, number of test cycles, number of patients, etc.).
• Data Provenance: The studies appear to be pre-market, non-clinical bench testing conducted by the manufacturer, Medtronic MiniMed, Inc. There is no indication of clinical study data or geographical origin of patient data (e.g., country of origin) as this particular submission focuses on the device and not a clinical study of its use. Many tests refer back to the predicate device (K251032).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not Applicable / Not Specified. The document describes non-clinical performance and engineering validation tests (e.g., accuracy, stability, cybersecurity, human factors). These types of tests typically rely on objective measurements against engineering specifications or regulatory requirements, rather than expert-established ground truth in the context of diagnostic interpretation. Human Factors validation involved intended users but the details about "experts" to establish a ground truth in a diagnostic sense are not present.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not Applicable / None Specified. This methodology (e.g., 2+1, 3+1 for clinical adjudication) is used for establishing ground truth in diagnostic studies, typically when evaluating algorithmic performance against human interpretation. The provided text describes engineering and regulatory compliance testing where such adjudication methods are not typically employed.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No. The provided text does not describe an MRMC comparative effectiveness study. This device is an insulin pump, not a diagnostic imaging AI system assisting human readers. The human factors validation is a separate type of study focusing on device usability and safety, not diagnostic performance improvement.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Yes, implicitly for many aspects. Many of the tests described are standalone performance evaluations of the device, its firmware, and its capabilities without human intervention beyond setting up the test (e.g., Delivery Volume Accuracy, Catheter Occlusion Detection, Data Logging, Cybersecurity, Software Verification). The bolus calculator's operation within the pump would also be a standalone algorithmic function based on user input. The "Manual Mode" and "Auto Mode" imply different levels of automation, but the core technical tests are often standalone.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• The type of "ground truth" varies by the specific test and is generally based on objective engineering and regulatory standards and reference methods.
  • Delivery Accuracy: Ground truth is the precisely measured or theoretical ideal insulin volume/rate against which the pump's actual delivery is compared.
  • Occlusion Detection: Ground truth would be the presence or absence of an occlusion under controlled test conditions.
  • Drug Stability/Compatibility: Ground truth is the chemical stability of insulin and the integrity of pump materials under test conditions.
  • Data Logging: Ground truth is the expected logging behavior as per design specifications and regulatory requirements.
  • Cybersecurity: Ground truth is the identified vulnerabilities and presence of effective mitigations.
  • Human Factors: Ground truth is the identification of safety-critical tasks and demonstration of safe and effective completion by intended users, often against predefined success criteria.

8. The sample size for the training set

• Not Specified / Not Applicable. The document does not describe a machine learning algorithm that undergoes a "training phase" with a specific dataset in the context of the device's development or regulatory submission. While the device contains firmware and potentially algorithms (like the iAGC algorithm embedded in the pump), the text focuses on verification and validation of the device itself against engineering specifications and regulatory controls, not the training of a learning algorithm. The iAGC is described as an embedded algorithm, but its training data or methodology are not part of this 510(k) summary.

9. How the ground truth for the training set was established

• Not Applicable. As no training set is described for a machine learning algorithm, the method for establishing its ground truth is not relevant to this document.

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SmartGuard technology is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible Medtronic continuous glucose monitors (CGMs), and alternate controller enabled (ACE) pumps to automatically adjust the delivery of basal insulin and to automatically deliver correction boluses based on sensor glucose values.

SmartGuard technology is intended for the management of Type 1 diabetes mellitus in persons 7 years of age and older, and of Type 2 diabetes mellitus in persons 18 years of age and older requiring insulin.

SmartGuard technology is intended for single patient use and requires a prescription.

Predictive Low Glucose technology is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible Medtronic continuous glucose monitors (CGMs), and alternate controller enabled (ACE) pumps to automatically suspend delivery of insulin when the sensor glucose value falls below or is predicted to fall below predefined threshold values.

Predictive Low Glucose technology is intended for the management of Type 1 diabetes mellitus in persons 7 years of age and older, and of Type 2 diabetes mellitus in persons 18 years of age and older requiring insulin.

Predictive Low Glucose technology is intended for single patient use and requires a prescription.

SmartGuard technology

SmartGuard technology, also referred to as Advanced Hybrid Closed Loop (AHCL) algorithm, is a software-only device intended for use by people with Type 1 diabetes, ages 7 years or older, and by people with Type 2 diabetes, ages 18 years or older. It is an interoperable automated glycemic controller (iAGC) that is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible interoperable Medtronic continuous glucose monitors (CGM) and compatible alternate controller enabled (ACE) pumps to automatically adjust the delivery of basal insulin and to automatically deliver correction boluses based on sensor glucose (SG) values.

The AHCL algorithm resides on the compatible ACE pump, which serves as the host device. It is meant to be integrated in a compatible ACE pump and is an embedded part of the ACE pump firmware.

Inputs to the AHCL algorithm (e.g., SG values, user inputs) are received from the ACE pump (host device), and outputs from the AHCL algorithm (e.g., insulin delivery commands) are sent by the algorithm to the ACE pump. As an embedded part of the firmware, the AHCL algorithm does not connect to or receive data from compatible CGMs; instead, sensor glucose (SG) values or other inputs received by the ACE pump from compatible CGMs via Bluetooth Low Energy (BLE) technology are transmitted to the embedded AHCL algorithm.

The AHCL algorithm works in conjunction with the ACE pump and is responsible for controlling insulin delivery when the ACE pump is in Auto Mode. It includes adaptive control algorithms that autonomously and continually adapt to the ever-changing insulin requirements of each individual.

The AHCL algorithm requires specific therapy settings (target setpoint, insulin-to-carb ratios and active insulin time) that need to be established with the help of a health care provider (HCP) before activation. It also requires five (5) consecutive hours of insulin delivery history, a minimum of two (2) days of total daily dose (TDD) of insulin, a valid sensor glucose (SG) and blood glucose (BG) values to start automated insulin delivery.

When activated, the AHCL algorithm adjusts the insulin dose at five-minute intervals based on CGM data. A basal insulin dose (auto basal) is commanded by the AHCL algorithm to manage glucose levels to the user's target setpoint of 100 mg/dL, 110 mg/dL or 120 mg/dL. The user can also set a temporary target of 150 mg/dL for up to 24 hours. In addition, under certain conditions the algorithm can also automatically command correction boluses (auto correction bolus) without user input.

Meal boluses are the responsibility of the user. The AHCL algorithm includes an integrated bolus calculation feature for user-initiated boluses for meals. When the user inputs their carbohydrate intake, the AHCL algorithm automatically calculates a bolus amount based off available glucose information, entered carbohydrate amount and other patient parameters.

The AHCL algorithm contains several layers of "safeguards" (mitigations) to provide protection against over-delivery or under-delivery of insulin to reduce risk of hypoglycemia and hyperglycemia, respectively.

The AHCL algorithm is a software-only device and does not have a user interface (UI). The compatible ACE pump provides a UI to the user to configure the therapy settings and interact with the algorithm. The AHCL-related alerts/alarms are displayed and managed by the pump.

Predictive Low Glucose technology

Predictive Low Glucose technology, also referred to as the Predictive Low Glucose Management (PLGM) algorithm is a software-only device intended for use by people with Type 1 diabetes, ages 7 years or older, and by people with Type 2 diabetes, ages 18 years or older. It is an interoperable automated glycemic controller (iAGC) that is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible interoperable Medtronic continuous glucose monitors (CGM) and compatible alternate controller enabled (ACE) pumps to automatically suspend delivery of insulin when the sensor glucose value falls below or is predicted to fall below predefined threshold values.

The PLGM algorithm resides on the compatible ACE Pump, which serves as the host device. It is meant to be integrated in a compatible ACE pump and is an embedded part of the ACE pump firmware.

Inputs to PLGM algorithm (e.g., sensor glucose values, user inputs) are received from the ACE pump (host device), and outputs from PLGM algorithm (e.g., suspend/resume commands) are sent by the algorithm to the ACE pump. As an embedded part of the ACE pump firmware, the PLGM algorithm does not connect to or receive data from compatible CGMs; instead, sensor glucose (SG) values or other inputs are received by the ACE pump from compatible CGMs via Bluetooth Low Energy (BLE) technology are transmitted to the embedded PLGM algorithm.

The PLGM algorithm works in conjunction with the ACE pump. When enabled, the PLGM algorithm is able to suspend insulin delivery for a minimum of 30 minutes and for a maximum of 2 hours based on current or predicted sensor glucose values. It will automatically resume insulin delivery when maximum suspend time of 2 hours has elapsed or when underlying conditions resolve. The user is also able to manually resume insulin at any time.

The PLGM algorithm is a software-only device and does not have a user interface (UI). The compatible ACE pump provides the UI to configure therapy settings and interact with the algorithm. The PLGM-related alerts/alarms are displayed and managed by the pump.

The provided FDA 510(k) clearance letter and summary concern the Medtronic SmartGuard Technology (Advanced Hybrid Closed Loop algorithm, AHCL) and Predictive Low Glucose Technology (Predictive Low Glucose Management algorithm, PLGM). The document details the devices' descriptions, indications for use, comparison to predicate devices, and summaries of non-clinical and clinical performance data.

However, it's important to note that this document primarily focuses on establishing substantial equivalence to previously cleared predicate devices and does not explicitly state specific acceptance criteria (performance targets) for clinical efficacy metrics (e.g., specific HbA1c reduction percentages or time in range targets needed for clearance) and does not present the study results in a direct "acceptance criteria vs. reported performance" table format for those specific targets. Instead, it highlights that the clinical data "confirmed the safety and effectiveness" and "demonstrated improved glycemic outcomes" or "non-inferiority," and that "the results also confirm that use...was associated with improved glucose control."

The document also provides details about the clinical studies without explicitly labelling them as "the study that proves the device meets the acceptance criteria" in the way a clinical trial protocol would specify primary and secondary endpoints and their statistical targets. Instead, it justifies substantial equivalence through the provided study data.

Therefore, the response below will extract the most relevant information based on your request, presenting the outcomes demonstrated by the studies as "reported device performance" where specific metrics are given, and noting the absence of explicit, pre-defined acceptance criteria targets in the clearance letter itself.

Acceptance Criteria and Study to Prove Device Meets Criteria

The FDA 510(k) summary for Medtronic's SmartGuard Technology and Predictive Low Glucose Technology establishes substantial equivalence to predicate devices. While the document asserts the safety and effectiveness, it does not explicitly define quantitative "acceptance criteria" for specific performance metrics in a pass/fail sense within this summary. Instead, it relies on demonstrating improved or non-inferior clinical outcomes compared to baseline or predicate performance. The clinical studies described confirm the safety and effectiveness and demonstrate associations with improved glucose control, which implicitly means the performance was deemed acceptable by the FDA for substantial equivalence.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

As explicit quantitative acceptance criteria (e.g., "HbA1c must reduce by X%") are not stated in this 510(k) summary, the table below presents the demonstrated clinical outcomes as "Reported Device Performance" highlighting the positive findings that supported clearance.

2. Sample Sizes Used for the Test Set and Data Provenance

The primary "test sets" for clinical effectiveness and safety were the patient cohorts in the described clinical studies.

• AHCL with Simplera Sync CGM (Type 1 Diabetes): The sample size details for this study (originally in K251217) are not explicitly provided in this 510(k) summary. However, it confirmed safety and effectiveness in Type 1 Diabetes patients.
• AHCL with Simplera Sync CGM & Guardian 4 CGM (Type 2 Diabetes) (P160017/S124):
  • Phase 1 (Guardian 4 CGM): N = 95 subjects.
  • Phase 2 (Simplera Sync sensor): N = 302 subjects (66 "transition", 236 "naive").
  • Data Provenance: Multi-center, single-arm study. The document does not specify countries but implies clinical trial settings. Given the general nature of FDA submissions, it would typically involve US-based and possibly international sites. Clinical data is generally considered prospective for such trials.
• AHCL with Guardian 4 CGM and Lyumjev Insulin (Type 1 Diabetes) (P160017/S125):
  • ITT Population: N = 101 (Age 7-17 Years), N = 110 (Age 18-80 Years).
  • Data Provenance: Single-arm, multi-center, home clinical investigation. The document does not specify countries but implies clinical trial settings. Prospective.
• AHCL with Guardian 4 CGM and Fiasp Insulin (Type 1 Diabetes) (P160017/S125):
  • ITT Population: N = 107 (Age 7-17 Years), N = 116 (Age 18-80 Years).
  • Data Provenance: Global multi-center, single-arm study. Countries mentioned: Australia, Canada, United States. Prospective.
• PLGM Algorithm: Evaluated for safety in a multi-center, single-arm, in-clinic study of the MiniMed 640G System. The sample size for this study (originally in K251217) is not explicitly provided in this 510(k) summary.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

The document refers to "clinical data" and "multi-center, single-arm studies" involving patients. For automated glycemic control devices, the "ground truth" for glucose values is typically established by laboratory reference methods (e.g., YSI glucose analyzer) performed by trained clinical staff as part of the study protocol, not by experts determining a ground truth in the interpretative sense. The efficacy endpoints (HbA1c, Time in Range, hypoglycemia) are derived from objective measurements, not subjective expert assessment of an image or signal.

There is no mention of "experts" in the context of establishing ground truth for glucose values or clinical outcomes. Clinical trials are monitored by clinical investigators (physicians, endocrinologists) and their teams, who ensure data integrity and protocol adherence, but they are not establishing a "ground truth" in the way radiologists might for AI image analysis.

4. Adjudication Method for the Test Set

Adjudication methods (e.g., 2+1, 3+1) are typically used in studies where subjective interpretation is involved, such as in reading medical images. For automated glycemic control devices, clinical outcomes are based on objective measurements (e.g., sensor glucose, lab-measured HbA1c). Therefore, the concept of an adjudication method as described does not directly apply to the clinical performance data presented here. Device-related adverse events would be reported and reviewed by the study investigators and likely an independent Data Safety Monitoring Board (DSMB), but this isn't an "adjudication method" for the primary clinical endpoints. The document does not explicitly describe an adjudication method for the objective clinical endpoints.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not done. MRMC studies are typically used to evaluate the performance of diagnostic devices or AI algorithms that assist human readers (e.g., radiologists interpreting images). The SmartGuard and PLGM technologies are automated insulin delivery algorithms, not diagnostic tools that human readers interpret. Therefore, the concept of "how much human readers improve with AI vs without AI assistance" does not apply here. The studies evaluate the algorithm's direct impact on glycemic control in patients.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

Yes, the core of the evaluation involves the "standalone" or "algorithm-only" performance in controlling glucose levels. The AHCL algorithm automatically adjusts basal insulin delivery and delivers correction boluses. The PLGM algorithm automatically suspends insulin delivery. While these systems require user input for meal boluses and setup, and interaction with alerts/alarms, the "SmartGuard technology" and "Predictive Low Glucose technology" themselves are software algorithms that function autonomously based on sensor glucose values to manage insulin delivery. The clinical studies assess the efficacy and safety of these algorithms in action within the device system (pump + CGM).

7. Type of Ground Truth Used

The ground truth for evaluating device performance in these studies is based on objective clinical measurements and patient outcomes. Specifically:

• Continuous Glucose Monitoring (CGM) sensor values: These are the primary input to the algorithm. While not explicitly stated as the ground truth for the algorithm's inputs, the accuracy of these values is critical and would have been established independently for the compatible CGMs.
• Laboratory-measured HbA1c: A standard clinical biomarker for average blood glucose over 2-3 months.
• Time-in-Range (TIR): Percentage of time spent with sensor glucose values within a target range (e.g., 70-180 mg/dL), derived from CGM data.
• Time-below-range (TBR): Percentage of time spent with sensor glucose values below a predefined threshold (e.g., <70 mg/dL), derived from CGM data.
• Adverse Events (AEs) and Serious Adverse Events (SAEs): Collected during clinical trials to assess safety.

These are physiological and clinical outcome data, not expert consensus or pathology reports in the typical sense.

8. The Sample Size for the Training Set

The document does not provide details on the sample size used for training the algorithm. This 510(k) summary focuses on the clinical data for validation of the finalized algorithms. The training set would be data used during the development phase of the algorithms, which is typically proprietary and not disclosed in 510(k) summaries. It would likely involve a large dataset of glucose profiles and insulin delivery patterns.

9. How the Ground Truth for the Training Set Was Established

Similarly, the document does not describe how the ground truth for the training set was established. For algorithms predicting glucose or controlling insulin, development and training would typically rely on:

• Retrospective or prospective real-world glucose and insulin data: Collected from individuals with diabetes using CGMs and insulin pumps, potentially under controlled conditions or in free-living settings.
• Validated glucose measurements: Such as YSI or other reference laboratory methods for blood glucose, and accurate CGM data.
• Clinical expert knowledge: Incorporating understanding of diabetes physiology, insulin pharmacokinetics/pharmacodynamics, and desired glycemic targets.
• Mathematical models of glucose metabolism: To simulate physiological responses and generate synthetic data for training.

The ground truth would be the actual glucose values and the physiological responses to insulin delivery, enabling the algorithm to learn patterns and predict future glucose trends or optimal insulin dosing.

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The Extended Reservoir is indicated for the subcutaneous infusion including insulin, from compatible Medtronic insulin pumps and infusion sets. Refer to your Medtronic insulin pump user guide for a list of compatible insulins and infusion sets.

Extended Reservoir (herein referred to as "EWR" or "MMT-342") is a sterile medication container designed for single use. The Extended Reservoir (MMT-342) is a component of the Medtronic Insulin Pump Delivery System used by patients with diabetes mellitus, requiring subcutaneous administered insulin, to maintain acceptable blood glucose levels. The Extended Reservoir (subject device) is indicated for the subcutaneous infusion of medication, including insulin, from compatible Medtronic insulin pumps and infusion sets. Refer to your Medtronic insulin pump user guide for a list of compatible insulins and infusion sets.

The provided text describes a 510(k) premarket notification for an "Extended Reservoir" (MMT-342) by Medtronic MiniMed, Inc. The primary purpose of this notification is to demonstrate substantial equivalence to a predicate device (Medtronic MiniMed Paradigm Reservoir MMT-332A, K032005) while extending the duration of use from 3 days to 7 days.

Here's an analysis of the acceptance criteria and study information:

Acceptance Criteria and Reported Device Performance

The core acceptance criterion is to demonstrate that extending the duration of use for the reservoir from 3 days to 7 days does not negatively impact the safety and effectiveness of the device. Since there are no changes in hardware design, materials, manufacturing, packaging, sterilization processes, fluid capacity, insulin compatibility, or reservoir assembly, the acceptance criteria are implicitly tied to maintaining the performance characteristics of the predicate device over the extended duration.

The text does not provide a specific table of quantitative acceptance criteria for parameters like insulin delivery accuracy, occlusion detection, or material degradation. Instead, it states that "The test results demonstrate that MMT-342 (subject device) met all the product requirements and specifications of MMT-332A (predicate device)." This implies that the performance over 7 days matches the established performance standards of the 3-day predicate.

The reported device performance, in summary, is that the Extended Reservoir (MMT-342) successfully maintains the same safety and effectiveness as the predicate device (MMT-332A) when used for up to 7 days.

Study Details

1. Sample size used for the test set and the data provenance:
   The document states: "Medtronic performed verification testing to support extending the duration of the reservoirs use (from up to 3 days to up to 7 days)." However, the specific sample size for this verification testing is not provided in the extracted text.
   The provenance of the data is not explicitly mentioned (e.g., country of origin, retrospective/prospective). It is implied to be internal testing conducted by Medtronic.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   This information is not provided in the extracted text. The verification testing described is likely technical performance testing rather than human expert assessment of a medical condition.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
   This information is not applicable as the document describes technical verification testing, not a clinical study involving adjudication of clinical observations.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
   This information is not applicable. The device is an insulin reservoir, not an AI-assisted diagnostic tool.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   This information is not applicable. The device is a physical medical device (insulin reservoir), not a software algorithm.

6. The type of ground truth used (expert concensus, pathology, outcomes data, etc.):
   For mechanical/material performance, the "ground truth" would be established engineering specifications and validated test methods (e.g., for infusion accuracy, material integrity, sterility maintenance). The document indicates that the subject device "met all the product requirements and specifications of MMT-332A." This implies the predicate device's established performance standards serve as the ground truth.

7. The sample size for the training set:
   This information is not applicable as the device is a physical medical device, not a machine learning model.

8. How the ground truth for the training set was established:
   This information is not applicable for the same reason as above.

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MiniMed Connect is intended to provide a secondary display of continuous glucose monitoring and/or insulin pump data on a suitable consumer electronic device to care partners and users of a MiniMed 530G system or Paradigm REAL-Time Revel system for the purpose of passive monitoring.

MiniMed Connect system is not intended to replace the real-time display of continuous glucose monitoring and/or insulin pump data on the primary display device (i.e. the sensor-augmented pump). All therapy decisions should be based on blood glucose measurements obtained from a blood glucose meter.

The MiniMed Connect is not intended to analyze or modify the continuous glucose monitor data and/or insulin pump data that it receives. Nor is it intended to control any function of the connecting continuous glucose monitor system and/or insulin pump. The MiniMed Connect is not intended to serve as a replacement for a primary display device for the continuous glucose monitoring system and/or insulin pump data. The MiniMed Connect is not intended to receive information directly from the sensor or transmitter of a continuous glucose monitoring system.

MiniMed® Connect is a secondary display of continuous glucose monitor and/or insulin pump data on a suitable consumer electronic device for insulin pump patients and their care partners. This system is designed as an optional accessory to compatible sensor-augmented pump systems.

MiniMed® Connect consists of a MiniMed® Connect app (for a local secondary display), the CareLink® Connect module of CareLink® Personal (for a remote secondary display), and the MiniMed® Connect uploader (for data transmission to the local app).

The MiniMed® Connect uploader is a small, battery-powered, ambulatory device that is carried with the patient in near proximity to the insulin pump. Its rechargeable battery is charged as needed (approximately once a day) using a USB Charger that accompanies the device.

The MiniMed® Connect uploader receives continuous glucose monitor and/or insulin pump data from the sensor-augmented insulin pump using a proprietary 916.5 MHz RF, and then converts it into a 2.4 GHz Bluetooth Low Energy (BLE) format. This BLE formatted data can then be read by the MiniMed® Connect app installed on a compatible consumer electronics device with BLE capabilities.

The MiniMed® Connect app reads the BLE data transmission and displays it on the patient's compatible consumer electronic device. The MiniMed® Connect app then uploads the continuous glucose monitor and/or insulin pump data to CareLink® Connect, the remote monitoring module of CareLink® Personal. Authorized care partners can access CareLink® Connect to view the patient's continuous glucose monitor and/or insulin pump data through an Internet-enabled consumer electronic device for the purpose of passive monitoring.

Accessories associated with this system include:

• USB Charger (for charging the MiniMed® Connect uploader) .

I am sorry, but the provided text does not contain the specific information required to answer your request regarding the acceptance criteria and the study that proves the device meets them. The document is an FDA 510(k) summary for the MiniMed Connect device, which primarily focuses on establishing substantial equivalence to a predicate device.

Specifically, the text states under "PERFORMANCE DATA [807.92(b)] VII.":

"Results of the verification and validation testing indicate that the product meets established performance requirements, and is safe and effective for its intended use."

However, it does not provide:

• A table of acceptance criteria and reported device performance.
• Details about the sample size, data provenance, number of experts, their qualifications, or adjudication methods for a test set.
• Information about MRMC comparative effectiveness studies, effect sizes, or standalone algorithm performance.
• The type of ground truth used, training set sample size, or how ground truth for the training set was established.

The document confirms that verification and validation testing was done and that the device met performance requirements, but it does not elaborate on what those requirements were or present the results of such testing.

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The Medtronic CareLink™ USB Connector is indicated for use by patients at home and clinicians in a medical office setting to facilitate communication between Medtronic diabetes ther apy management devices that use Paradigm-compatible RF telemetry and a personal computer that uses data management application software.

The Medtronic MiniMed CareLink USB Connector is an accessory device that facilitates wireless communication between compatible Medtronic MiniMed radiofrequency telemetry devices and a personal computer. The hardware component of the CareLink USB Connector consists of a radio-frequency (RF) transceiver enclosed in a plastic housing and one USB connector that is compatible with a type A (female) USB port of a personal computer (PC) or a USB hub. The CareLink USB Connector has a form factor similar to a USB flash memory stick and will by recognized by the PC as a USB device. The CareLink USB Connector is designed for use with Medtronic MiniMed devices that use Paradigm radiofrequency telemetry. Data is transferred between Medtronic MiniMed Paradigm RF compatible devices and a personal computer (PC) using select Medtronic MiniMed data management software applications.

The provided text is related to a 510(k) premarket notification for the Medtronic MiniMed CareLink™ USB Connector (Model MMT-7305). This device is an accessory that facilitates wireless communication between Medtronic MiniMed radiofrequency telemetry devices and a personal computer. The 510(k) summary focuses on comparing this new device to a predicate device (Com-Link Communication System, Model MMT-7304) and asserting substantial equivalence.

Crucially, this document does not describe any performance acceptance criteria for the device itself, nor does it detail any study that proves the device meets such criteria in terms of clinical or diagnostic accuracy. The focus is on the device's technological features and its intended use as a communication facilitator, not on a diagnostic or therapeutic output that would require a performance study with acceptance criteria.

Therefore, most of the requested information cannot be extracted from the provided text.

Here is what can be inferred or directly stated based on the given document:

1. A table of acceptance criteria and the reported device performance

No explicit acceptance criteria or reported device performance metrics (such as sensitivity, specificity, accuracy, etc.) are mentioned in the provided text. The submission is a 510(k) for substantial equivalence based on technological features and intended use, not clinical performance.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not applicable. No performance study data is presented. The submission focuses on device design and comparison to a predicate.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. No ground truth establishment or expert review for a test set is mentioned.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. No adjudication methods for a test set are mentioned.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This device is not an AI-assisted diagnostic tool, and no MRMC study or AI assistance is mentioned.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. This device is a communication connector, not an algorithm with standalone performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable. No ground truth for performance evaluation is mentioned.

8. The sample size for the training set

Not applicable. No training set for an algorithm is mentioned.

9. How the ground truth for the training set was established

Not applicable. No ground truth for a training set is mentioned.

Summary:

The provided document is a regulatory submission (510(k)) focused on demonstrating substantial equivalence of a new medical device (a USB connector for data transfer) to an existing predicate device. The primary argument for equivalence is based on similar function and technology, with the main difference being the physical connection type (USB vs. serial port). This type of submission typically does not involve extensive clinical performance studies or the establishment of ground truth for diagnostic accuracy, as the device's function is data communication rather than a diagnostic or therapeutic intervention itself. Therefore, the requested information regarding acceptance criteria and performance studies is not present in the given text.

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