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The CERA-CHEK 1070 Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertip, forearm, upper arm, palm, thigh, or calf. The CERA-CHEK 1070 Blood Glucose Monitoring System is intended to be used by a single person and should not be shared. Alternative site testing should be done only during steady-state times (when glucose is not changing rapidly).

The CERA-CHEK 1070 Blood Glucose Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes controls. The CERA-CHEK 1070 Blood Glucose Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use.

The CERA-CHEK 1070 Blood Glucose Test Strips are for use with the CERA-CHEK 1070 Blood Glucose Test Meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertip, forearm, upper arm, palm, thigh, or calf.

The CERA-CHEK 1070 Control Solution is for use with the CERA-CHEK 1070 Blood Glucose Test Meter and Test strips as a quality control check to verify that the meter and test strips are working together properly and that the test is performing correctly.

The CERA-CHEK Diabetes Management Software is PC-based software intended for use in home and professional settings to help people with diabetes and their healthcare professionals in the review, analysis and evaluation of glucose results for effective diabetes management. It is intended for use as an accessory to compatible CERAGEM MEDISYS blood glucose monitoring systems. The CERA-CHEK Diabetes Management Software's language is English.

The CERA-CHEK 1070 Blood Glucose Monitoring System consists of the CERA-CHEK 1070 Glucose Test Meter, CERA-CHEK 1070 Blood Glucose Test Strips with Code Key, CERA-CHEK 1070 Control Solution 1 and Control Solution 2, a Lancing device, and CERA-CHEK Diabetes Management Software and cable needed for installing the software on the PC and for transmitting data from meter. Control Solution 1 and Control Solution 2 are required but not included with the meter. Control Solution 1 and Control Solution 2 are always provided as a set. CERA-CHEK Diabetes Management Software and cable are required but not included with the meter. CERA-CHEK Diabetes Management Software and cable are always provided as a set.

1. Acceptance Criteria and Reported Device Performance:

The document primarily focuses on the analytical performance and system accuracy of the CERA-CHEK 1070 Blood Glucose Monitoring System. The acceptance criteria are implicitly defined by the reported performance relative to a reference method (YSI 2300 analyzer) and the ranges tested.

Table 1: Acceptance Criteria (Implied) and Reported Device Performance

• CV (%) range: 2.2% - 5.2% |
  | Day-to-day Precision | Low CV% for control samples across different glucose levels | Glucose concentration 43-304 mg/dL:
• CV (%) range: 2.8% - 5.5% |
  | Linearity (r^2) | Close to 1.0, supporting claimed measurement range | Slope: 0.9782 - 0.9896
  Intercept: 1.4433 - 4.4741
  Corr Coeff (r^2): 0.9992 - 0.9997 (supports 20-600 mg/dL range) |
  | Measurement Range | 20-600 mg/dL | 20-600 mg/dL (validated by linearity study) |
  | System Accuracy (Technician vs YSI) | = 75 mg/dL: 100% within +/-20%; high percentage within +/-5%/10%/15% | = 75 mg/dL (n=171):
• 58% within +/-5%
• 82% within +/-10%
• 96% within +/-15%
• 100% within +/-20% |
  | System Accuracy (Lay User vs YSI) | = 75 mg/dL: 100% within +/-20%; high percentage within +/-5%/10%/15% | = 75 mg/dL (n=162):
• 56% within +/-5%
• 76% within +/-10%
• 94% within +/-15%
• 100% within +/-20% |
  | Alternate Site Testing | Similar accuracy to fingertip testing, within specified error margins for professional and lay users. | Professional (n=5 for =75): Generally good accuracy across palm, forearm, upper arm, thigh, calf (e.g., 93-98% within +/-15% for >=75 mg/dL).
  Lay User (n=5 for =75): Generally good accuracy across palm, forearm, upper arm, thigh, calf (e.g., 93-97% within +/-15% for >=75 mg/dL). |
  | Hematocrit Range | Accurate results over the claimed range (10 - 70%) | Demonstrated accurate results for 10-70% hematocrit. |
  | Altitude | Accurate results up to 13,200 feet | Demonstrated accurate results up to 13,200 feet. |
  | Temperature and Humidity | Accurate results across claimed temperature (10-40°C) and humidity (10-85%) ranges | Demonstrated acceptable bias for 10-40°C and 10-85% RH. |
  | Interference | No significant interference from listed substances at specified concentrations | Listed substances found not to interfere at specified concentrations (e.g., Acetaminophen up to 6 mg/dL, Bilirubin up to 4 mg/dL, Triglyceride up to 1,500 mg/dL). Specific limitations noted for dopamine, methyldopa, tolazamide, and xylose. |
  | Shelf-life Stability | 24 months (closed vial); 4 months (open vial) | Test strip shelf-life of 24 months (closed vial) and 4 months (open vial) supported. |
  | Control Solution Stability | 12 months (closed vial); 4 months (open vial) | Control solution shelf-life of 12 months (closed vial) and 4 months (open vial) supported. |

2. Sample Sizes and Data Provenance:

• Test Set Sample Sizes:
  • Within-run Precision: For each of 5 glucose concentrations, 5 strip lots, and 10 meters, there were 10 measurements per strip lot per meter. This totals 5 * 5 * 10 * 10 = 2500 measurements. (The text states "a total of 100 measurements per glucose concentration", which seems to contradict the previous sentence unless it's per meter type across strip lots, or a summary. Taking the more detailed description, it's 2500 total).
  • Day-to-day Precision: For each of 3 control levels, 1 strip lot (per glucose level, so effectively 3 lots used in total), and 10 meters, measured once per day over 20 days. This totals 3 * 1 * 10 * 20 = 600 measurements per each of the three levels, for a grand total of 1,800 measurements.
  • Linearity/Assay Reportable Range: 9 (or 10) glucose samples, each analyzed 5 times using 3 lots of test strips. This would be 9 (or 10) * 5 * 3 = 135 to 150 measurements.
  • System Accuracy Study (Technician vs YSI):
    • Fingertip samples: 200 participants (collected and tested twice by themselves, and once by healthcare professional).
    • Contrived samples: 20 samples.
    • Total sample comparisons to YSI for technician: 171 (≥75 mg/dL) + 47 (= 75 mg/dL, for both professional and lay users.
  • Hematocrit Study: Five measurements for each combination of glucose concentration and hematocrit level (specific total N not given, but glucose range 21-529 mg/dL and hematocrit range 10-70%).
  • Altitude Study: Three lots of test strips and three meters were used (specific total N not given, but 3 glucose concentrations).
  • Temperature and Humidity Studies: Three test strip lots, three glucose concentrations, twelve combinations of temp/humidity, replicates of three for each combination/glucose/meter (specific total N not given).
  • Interference Studies: Whole blood from healthy volunteers (exact number of volunteers not specified, but multiple glucose levels and interferent concentrations tested).
• Data Provenance: The document does not explicitly state the country of origin for the clinical study data or whether it was retrospective or prospective. However, given the manufacturer is based in Korea (Republic of Korea), it is highly probable the studies were conducted there. The studies appear to be prospectively designed clinical and analytical performance studies conducted specifically for this submission.

3. Number of Experts and Qualifications for Ground Truth: No specific number of experts are explicitly stated as establishing the ground truth for the test set in the way one might for diagnostic imaging studies.

• For the analytical and system accuracy studies, the "ground truth" was established by laboratory reference methods, specifically the YSI 2300 Glucose analyzer for glucose measurements. The YSI 2300 is calibrated using a NIST traceable glucose standard, implying a highly accurate and standardized method for ground truth determination.
• For the usability study, "untrained lay users" were involved, but their assessment was on the "readability of the labeling" and "ease of use," not on establishing diagnostic ground truth.

4. Adjudication Method for the Test Set: Not applicable. The ground truth for the glucose measurements was established by a single, highly accurate laboratory reference method (YSI 2300), not through a consensus or adjudication process among multiple human experts.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: No, an MRMC comparative effectiveness study was not done. The studies assessed the standalone performance of the device (both technician and lay user operated) against a reference method. There is no comparison of "human readers improve with AI vs without AI assistance" as this is a blood glucose meter, not an AI-assisted diagnostic imaging device for human interpretation.

6. Standalone (Algorithm Only) Performance: Yes, standalone performance was done for the device in the context of a blood glucose meter. The "Technician vs YSI" data directly represents the standalone performance of the device when operated by a trained professional against the gold standard (YSI). The "Lay User vs YSI" data represents the performance when operated by the intended end-user.

7. Type of Ground Truth Used:
The primary ground truth used throughout the performance studies (precision, linearity, system accuracy, alternate site testing, hematocrit, altitude, temperature/humidity, interference) was laboratory reference method measurements obtained from the YSI 2300 Glucose analyzer, which is calibrated using a NIST traceable glucose standard. This is a highly objective and quantitative ground truth.

8. Sample Size for the Training Set: The document does not specify a separate "training set" sample size. For medical devices like blood glucose meters, the development and calibration ("training") of the device's algorithms or underlying chemical reactions often occur prior to these validation studies. These studies primarily serve as external validation or "test sets" to demonstrate the final product's performance. The information provided heavily details these validation studies.

9. How Ground Truth for the Training Set Was Established: As above, specific details on a "training set" and its ground truth establishment are not provided in this 510(k) summary. The development process would typically involve extensive internal testing and calibration against reference methods (like the YSI 2300) to fine-tune the device's performance before formal validation studies are conducted. The traceability to the YSI 2300 analyzer and NIST traceable glucose standard suggests that this highly accurate reference method would have been central to any internal calibration or "training" process.

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Intel-GE Care Innovations Connect RCM is intended to collect vital sign measurements from physiological measurement devices intended for use in the home. Patients can review the stored vital sign measurement information and receive educational and motivational content from caregivers. Patients can also engage in videoconferences with caregivers and answer the caregivers' questions by participating in surveys.

Care Innovations Connect RCM is not interpretive, nor is it intended for diagnosis or as a substitute for medical care, and it is not intended to provide real time data. It is made available to patients when time-critical care is not required.

Care Innovations Connect RCM is contraindicated for patients requiring direct medical supervision or emergency intervention. It is intended for patients who are willing and capable of managing its use. Clinical judgment and experience by a caregiver are required to check and interpret the information delivered.

Care Innovations Connect RCM will be available for over the counter use.

Care Innovations™ Connect RCM is a software application for use with measurement devices commercially available for home use. The software executes on a web server and is accessed via a browser from the patient's COTS personal computing device. A small validated software application known as Device Connector runs on patients' home COTS platforms. Off the Shelf (OTS) software is also used with the internally developed software to provide functionality such as: setting & receiving email and text-based notifications, creating & editing calendar entries, playing Learn More videos, and holding a video conference with a clinician.

Care Innovations™ Connect RCM is a software application for use with measurement devices commercially available for home use. Connect RCM provides the same client capabilities of collecting and transmitting patient data to the clinician database system as the predicate devices, and uses the existing clinician database system in the Intel-GE Care Innovations 10 Guide (K130290). No changes were required to the existing clinician database system to support the new client software.

Patients can also enter measurement data manually entered data is stored in the backend clinician database as well as the Personal Health Data Record. It is flagged as manually entered data.

The provided document, K130821, states that the "Connect RCM does not rely on an assessment of clinical performance data." It asserts that "The device will conform to FDA 's recognized consensus standards and relies on its conformity to demonstrate the safety and efficacy." Therefore, there is no study described that proves the device meets specific acceptance criteria based on clinical performance.

Instead, the submission relies on demonstrating substantial equivalence to predicate devices and conformity to recognized consensus standards for safety and efficacy.

Given this, I cannot fill out the requested table or answer most of the follow-up questions because the submission explicitly states that clinical performance data was not used.

Here's what I can provide based on the document:

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

The document does not provide a table of acceptance criteria and reported device performance in the context of clinical studies. The "acceptance criteria" for this device appear to be its conformity to FDA recognized consensus standards and its substantial equivalence to predicate devices.

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

Not applicable. The device does not rely on clinical performance data for its 510(k) submission.

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

Not applicable. No clinical test set described.

4. Adjudication method

Not applicable. No clinical test set described.

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. The device is a remote patient monitoring system, not an AI-assisted diagnostic tool, and no such study was performed or described.

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

Not applicable. The device is software for collecting and displaying patient data, not an algorithm for standalone performance evaluation in a clinical sense.

7. The type of ground truth used

Not applicable. No clinical performance data was used for the 510(k) submission. The "ground truth" for regulatory approval appears to be the documented functionality of the predicate device and the adherence to safety standards.

8. The sample size for the training set

Not applicable. No machine learning model or training set is described in the context of clinical performance for this 510(k).

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

Not applicable. No training set is described.

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The CONTOUR®NEXT EZ blood glucose monitoring system is an over the counter (OTC) device utilized for self-testing by persons with diabetes at home for the quantitative measurement of glucose in whole blood, is for single-patient use only, and should not be shared. The CONTOUR NEXT EZ blood glucose monitoring system is indicated for use with fresh fingertip capillary whole blood samples. The clinical utility of this device is to aid in monitoring the effectiveness of your diabetes control program.

The CONTOUR® NEXT EZ blood glucose monitoring system is not intended for use for the diagnosis of or screening for diabetes mellitus and is not intended for use on neonates.

The CONTOUR® NEXT test strips are intended for self-testing by persons with diabetes for the quantitative measurement of glucose in whole blood samples from 20 to 600 mg/dL.

The CONTOUR®NEXT control solutions are aqueous glucose solutions intended for use in self-testing by people with diabetes as a quality control check.

The Contour NEXT EZ Blood Glucose Monitoring System consists of:

1. Contour NEXT EZ Blood Glucose Meter
2. Contour NEXT Blood Glucose Test Strips
3. Contour NEXT Control Solutions

Acceptance Criteria and Device Performance Study for Contour NEXT EZ Blood Glucose Monitoring System

1. Table of Acceptance Criteria and Reported Device Performance

• Data Provenance: Laboratory-controlled fresh venous blood.

Detection Limit Study:

• Sample Size: 8 Contour meters (3 readings per meter) with 3 test strip lots; 72 readings for low glucose (5 mg/dL) and 288 readings for high glucose (900-1800 mg/dL).
• Data Provenance: Not explicitly stated, but implies laboratory-controlled blood samples.

Analytical Specificity (Hematocrit) Study:

• Sample Size: Fresh venous blood samples (15% to 65% hematocrit), adjusted to two plasma glucose concentrations (40 mg/dL and 550 mg/dL). 12 replicates per sample collected on six Contour NEXT meters for each of three test strip lots.
• Data Provenance: Laboratory-controlled fresh venous blood samples.

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

• Ground Truth Method: The ground truth for all performance studies (Accuracy, Precision, Linearity, Detection Limit, Analytical Specificity) was established by comparison to the YSI 2300 STAT PLUS glucose analyzer.
• Expert involvement: The document does not specify direct involvement of human "experts" to establish the ground truth for individual test set samples. Instead, the YSI appliance serves as the reference standard. The traceability section states the YSI analyzer is traceable to the hexokinase method, which was developed collaboratively by the FDA, CDC, NIST, and AACC. This implies expert validation of the reference method, rather than individual sample adjudication by experts.

4. Adjudication Method for the Test Set

• None specified for individual samples. The YSI 2300 STAT PLUS glucose analyzer provided a single reference value for each blood sample, acting as the definitive ground truth. There was no mention of multiple expert reads or an adjudication process for discrepancy resolution for the test samples themselves.

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

• No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was described. The studies focused on the performance of the device itself against a reference standard or internal criteria. The clinical study involved users self-testing, but it was not designed as an MRMC study to compare human readers' performance with and without AI assistance.

6. Standalone (Algorithm Only) Performance Study

• Yes, standalone performance was done. The "Accuracy" section under "Performance Data" describes "System Accuracy Evaluation" where "Six Contour NEXT EZ meters, three lots of test strips and 100 blood samples were tested in replicates." The reported results are directly from the device's measurement compared to the YSI reference, indicating standalone algorithm performance. Similarly, all other non-clinical studies (Precision, Linearity, Detection Limit, Analytical Specificity) evaluate the inherent performance of the device's system and algorithm.

7. Type of Ground Truth Used

• YSI 2300 STAT PLUS glucose analyzer results. This is stated to be traceable to the hexokinase method, which is a recognized laboratory reference method for glucose measurement. The hexokinase method itself is "incorporated in a Bayer procedure that utilizes NIST Standard Reference Material 917, dry D-glucose." This indicates a highly accurate and standardized laboratory reference.

8. Sample Size for the Training Set

• Not explicitly stated. The document is a 510(k) summary for a blood glucose monitoring system, which typically relies on established electrochemical principles rather than machine learning algorithms that require large training sets in the same way AI-powered image analysis tools do. The "measurement algorithm" was modified, but the detail on its development and any associated "training set" (in the sense of machine learning) is not provided in this summary. Instead, the focus is on verification and validation studies using test sets.

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

• Not applicable/Not explicitly stated in the context of machine learning training data. As mentioned above, the device likely relies on a deterministic algorithm for glucose measurement based on electrochemical detection. Therefore, the concept of a "training set" with established ground truth as used in machine learning is not directly addressed. The device is verified and validated against established laboratory reference methods and internal criteria.

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The Intel® Health Guide Express is intended to collect vital sign measurements from physiological measurement devices intended for use in the home. Patients can review the stored vital sign measurement information and receive educational and motivational content from caregivers. Patients can also engage in video conferences with caregivers and answer the caregivers' questions by participating in surveys.

The Intel® Health Care Management Suite allows the caregiver to review patient data and initiate video conferencing with patients, or select and send educational and motivational content to patients.

The Intel® Health Guide Express is not interpretive, nor is it intended for diagnosis or as a substitute for medical care, and it is not intended to provide real time data. It is made available to patients when time-critical care is not required.

The Intel® Health Guide Express is contraindicated for patients requiring direct medical supervision or emergency intervention. It is intended for patients who are willing and capable of managing its use. Clinical judgment and experience by a caregiver are required to check and interpret the information delivered.

The Intel® Health Guide Express is a communication tool that allows caregivers to remotely access vital sign measurements of patients at home. The Intel® Health Guide Express is a software application running on a Commercial Off The Shelf (COTS) Personal Computer (PC). It collects measurements captured on commercially available wireless or tethered medical devices which are designed for home use and connection to a COTS PC. It displays the collected measurement on the PC, and securely stores the collected information locally on a memory device installed in the PC. The Intel® Health Guide Express also stores the information remotely on a host server, where the caregiver can view the measurement via the host server once synchronization between the host server and Intel® Health Guide Express has been completed. The Intel® Health Guide Express can be used to display educational and motivational content from the caregiver and can facilitate communication between the caregiver and patient via health wellness surveys and optional video conferencing.

The Intel® Health Guide Express is not interpretive, nor is it intended for diagnosis or as a substitute for medical care, and it is not intended to provide real time data. It is made available to patients when time-critical care is not required. It is contraindicated for patients requiring direct medical supervision or emergency intervention. It is intended for patients who are willing and capable of managing its use. Clinical judgment and experience by a caregiver are required to check and interpret the information delivered.

The Intel® Health Guide PHS Express system consists of the:

• Intel® Health Guide Express software application: (1)
  The software application captures, stores, displays and transmits information to a secure database on a host server running the Intel® Health Care Management Suite software via a standard telephone line or internet connection. The Intel® Health Guide Express software runs on a Commercial Off The Shelf (COTS) Personal Computer (PC).

Intel® Health Care Management Suite software application: (2)

The software application runs on a host server and allows caregivers to review patient vital signs on the secure website. The Intel® Health Care Management Suite allows for predefining upper and lower limits and, when either limit is exceeded, the system emails and/or pages the caregiver.

The Intel® Health Guide Express is a remote patient monitoring system. The provided text is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than detailing specific acceptance criteria and a study to prove meeting them in the way clinical studies for diagnostic accuracy often do.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" and "reported device performance" in the context of diagnostic accuracy or a specific performance study. Instead, it demonstrates substantial equivalence to a predicate device (Intel® Health Guide PHS6000) based on technological characteristics and functional similarities. The key performance aspect is the ability to collect, store, display, and transmit vital sign measurements, and this is compared to the predicate device's capabilities and compatibility with various peripherals.

Essentially, the "acceptance criteria" here are that the new device performs its intended functions (collecting and transmitting data) and is at least as safe and effective as the predicate device, given its specific use case as a communication tool and not a diagnostic device.

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

The document does not describe a specific "test set" or a study involving patient data for performance evaluation in the context of diagnostic accuracy or a similar clinical measurement. The device is a "Remote Patient Monitoring System" that collects data from other commercially available medical devices.

The assessment is primarily a technical comparison and declaration of substantial equivalence to a predicate, not a clinical trial evaluating the performance on a patient cohort or a specific dataset. There is no mention of data provenance (country of origin, retrospective/prospective).

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

This information is not applicable as there is no mention of a "test set" requiring ground truth established by experts for performance evaluation. The device's function is data capture and transmission, not interpretation or diagnosis.

4. Adjudication Method for the Test Set

This information is not applicable for the same reason as point 3. No test set requiring expert adjudication for ground truth establishing is described.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

No, a MRMC comparative effectiveness study was not done. The device is a data collection and communication tool, and its primary purpose is not to assist human readers in interpretation or diagnosis. Therefore, a study of improved human reader performance with AI assistance is not relevant to this type of device.

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

No, a standalone study in the context of algorithmic performance for diagnosis/interpretation was not described. The device is a software application running on a COTS PC that interacts with other medical devices. Its performance is assessed in terms of its ability to correctly capture, store, transmit data, and its compatibility with hardware, rather than standalone diagnostic accuracy. The safety and efficacy claims "do not rely on an assessment of clinical performance data" but on conformity to recognized consensus standards.

7. The Type of Ground Truth Used

Not applicable. As stated earlier, the submission focuses on substantial equivalence based on technological characteristics and safety standards, not on evaluating diagnostic accuracy against a ground truth. The device itself is "not interpretive, nor is it intended for diagnosis."

8. The Sample Size for the Training Set

Not applicable. This device is a remote patient monitoring software system. There is no mention of a "training set" for an AI algorithm in the context of diagnosis or prediction.

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

Not applicable. For the same reasons as point 8, there is no mention of a training set or ground truth establishment relevant to an AI model's training.

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The Didget blood glucose monitoring system (meter, strips, and controls) is intended for self-testing by people with diabetes to monitor glucose concentrations in fresh capillary whole blood samples drawn from the fingertip only. It is intended for those ages four and older, with adult supervision as needed. The Didget blood glucose monitoring system is not intended for the diagnosis of or screening for diabetes mellitus and is not intended for use on neonates.

The Didget Blood Glucose Monitoring System consists of:

1. Didget Blood Glucose Monitor
2. Contour Blood Glucose Test Strips
3. Contour Control Solution

The provided text describes a 510(k) summary for the Didget Blood Glucose Monitoring System. It outlines the device, its intended use, and a performance assessment, but does not specify numerical acceptance criteria or detail the study results required to prove these criteria were met. The document states that "The studies showed equivalent performance with the current Contour system." and "The results of clinical evaluations... demonstrated that the device can produce blood glucose results that are substantially equivalent to results obtained on the predicate device." However, specific metrics, thresholds, and statistical analyses are not provided.

Therefore, many of the requested details cannot be extracted from the provided text.

Here is a summary of what can be extracted and what cannot:

1. Table of acceptance criteria and the reported device performance:

• Note: The document does not provide specific numerical acceptance criteria (e.g., accuracy percentages, bias limits) or the detailed numerical results from the Didget system's performance. It broadly states "equivalent performance".

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

• Sample Size for Test Set: Not specified. The document states "a clinical setting using persons with diabetes, ages 5 through 24," but does not give a number of participants.
• Data Provenance:
  • Country of origin of the data: Not specified.
  • Retrospective or prospective: Not specified, but "studied in the laboratory and in a clinical setting" suggests a prospective study.

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

• Not applicable. This study is for a blood glucose meter, which typically uses a highly accurate laboratory reference method (e.g., YSI analyzer) as the "ground truth" rather than expert interpretation of images or other data. The text mentions "compared to a laboratory method."

4. Adjudication method for the test set:

• Not applicable. (See point 3)

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. This is not relevant to a blood glucose meter study. This type of study is typically done for diagnostic imaging or interpretation tasks involving human readers.

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

• Yes, effectively. The performance assessment of a blood glucose meter is inherently a standalone assessment of the device's accuracy against a reference method. The Didget system's performance was compared to the Contour system and "a laboratory method."

7. The type of ground truth used:

• Laboratory Method: The document states the results were "compared to... a laboratory method." This typically refers to a highly accurate and precise analytical instrument considered the gold standard for glucose measurement.

8. The sample size for the training set:

• Not applicable / Not specified. Blood glucose meters are not typically "trained" in the same way machine learning algorithms are. Their performance is based on the chemical and electrochemical reactions of the test strip and the meter's electronics.

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

• Not applicable / Not specified. (See point 8)

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