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The Compex HD is intended to be used by a dentist or health care provider to warm and dispense dental composite materials contained in a compule or PLT (Pre Loaded Tip) to 155 degrees F (68 degrees C).

The Compex HD uses a heating device to elevate the temperature of dental composite material contained in a compule (PLT) to allow it to flow and be easily extruded. It utilizes a plastic housing in the shape of a traditional dispensing qun. The housing contains a heater element(s) and/or an electrically conductive plastic compule that itself acts as a heater when a current is applied. A control circuit board assembly and rechargeable lithium lon battery are used to apply power to the heater. A separate wall plug-in USB power supply is used to charge the battery. The unit will not function with the USB power supply connected to the unit.

The provided text describes the Compex HD device, a composite warmer for dental materials, and compares it to a predicate device, the Calset. It focuses on non-clinical performance data to demonstrate substantial equivalence, rather than a detailed AI algorithm performance study. Therefore, some of the requested information, particularly related to AI-specific metrics like human reader improvement with AI assistance, MRMC studies, or training set details, is not applicable to this device.

Here's a breakdown of the available information based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document refers to a "test set" in the context of comparing the Compex HD and Calset devices.

• Sample Size: The number of compules/PLTs tested is not explicitly stated. It mentions "testing was performed on both devices using thermocouples inserted into composite filled compules."
• Data Provenance: The tests were conducted internally by the manufacturer, AdDent, Inc. The data is retrospective, as it's part of the 510(k) submission. No country of origin for the data is specified beyond the company's location in Danbury, Connecticut, USA.

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

• Not Applicable. This device is a warming and dispensing tool, not an AI diagnostic or decision support system that requires expert interpretation for ground truth. The performance evaluation relies on physical measurements (temperature, flow, safety standards compliance) rather than expert consensus on diagnostic accuracy.

4. Adjudication Method for the Test Set

• Not Applicable. As the performance evaluation involves objective physical measurements and adherence to engineering standards, there is no need for an adjudication method typically used for subjective diagnostic interpretations.

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-powered system, and no MRMC study or assessment of human reader improvement with AI assistance was conducted or is relevant.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Not Applicable. This device does not involve an algorithm for standalone performance evaluation in the context of AI. Its performance is evaluated mechanically and thermally.

7. The Type of Ground Truth Used

• The "ground truth" for the performance evaluation primarily consists of:
  • Direct Physical Measurements: Thermocouple readings to verify the composite temperature reached 155°F (68°C).
  • Engineering Standards Compliance: Successful adherence to international safety and EMC standards (ANSI/AAMI/IEC 60601-1 and ANSI/AAMI/IEC 60601-1-2).
  • Qualitative Assessment: Observation of improved flow of heated composite and ease of dispensing.

8. The Sample Size for the Training Set

• Not Applicable. This device is not an AI/machine learning device, so there is no "training set" in the conventional sense.

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

• Not Applicable. As there is no training set for an AI algorithm, no ground truth was established for it.

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Intended Use: The Calset unit is used to warm dental composite materials and whitening bleach to130°F (54°C) or 155°F (68°C). It is also used to warm anesthetic carpules to 98°F (37°C) (body temperature).

The Calset is a warming device used to heat dental materials prior to their use in the mouth. It consists of a base unit that contains a heating element a temperature control circuit with LED indicators, a removable metal tray that is designed to hold different materials and a low voltage wall transformer that provides power to the unit.

Here's an analysis of the provided text regarding the Calset Composite Heater, focusing on the acceptance criteria and study information:

The provided text is a 510(k) summary for a medical device called the "Calset Composite Heater." This document is used to demonstrate substantial equivalence to a predicate device, not to provide detailed study results for performance acceptance criteria in the way one might find for a novel, high-risk device requiring extensive clinical trials.

Therefore, many of the typical questions about AI/ML algorithm studies (like training set size, ground truth for training, MRMC studies, specific performance metrics for algorithms) are not applicable to this type of submission. The focus here is on demonstrating the device functions as intended and is safe, primarily through comparison to an already-approved predicate device and basic performance testing.

Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the intended use and the comparison to the predicate device. The performance is reported directly in the description.

Study that Proves the Device Meets Acceptance Criteria:

The document describes the device and its intended uses, and implicitly relies on the technical specifications and comparison to the predicate device to prove it meets the criteria. It does not provide a detailed "study" in the sense of a clinical trial or a performance study with a separate test set, ground truth, or statistical analysis. This is typical for Class I or Class II devices undergoing 510(k) clearance, where substantial equivalence is the primary focus.

The "study" here is more of an engineering verification that the device achieves the stated temperatures.

Additional Requested Information:

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

   • N/A. The document does not describe a "test set" in the context of clinical data or image data. The performance claims are based on the device's engineering specifications. There is no indication of clinical data provenance.

2. 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):

   • N/A. Ground truth for a diagnostic or AI algorithm is not relevant here. The ground truth for device performance would be a calibrated thermometer measuring the actual temperature achieved by the heater.

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

   • N/A. Not applicable, as there is no test set in the context of diagnostic or AI-driven performance.

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:

   • N/A. This device is a composite heater, not an AI-assisted diagnostic tool. Therefore, MRMC studies are not applicable.

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

   • N/A. This is a hardware device; there is no embedded algorithm in the sense of AI/ML that would have standalone performance. Its "performance" is its ability to heat to specified temperatures, which is a physical characteristic.

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

   • For the temperature claims, the ground truth would be physical measurement with calibrated instruments (e.g., thermometers). This is not explicitly stated but is implicitly assumed for any device that makes temperature claims.

7. The sample size for the training set:

   • N/A. There is no training set mentioned or implied, as this is not an AI/ML device.

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

   • N/A. No training set exists.

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The Transonic Syringe Warmer (Model SYR1000) is indicated for use with the Transonic HD01 (HD01PMS and HD02) systems to make Cardiac output measurements. The Transonic Syringe Warmer (Model SYR1000) is an accessory to our HD01-CO Hemodialysis Monitor for Cardiac Output (K980906) which are currently marketed as the HD01046 and HD02 systems. The HD01-CO Hemodialysis Monitor for Cardiac Output (HD01P05 and HD02 systems) requires the user to make a 30cc injection of approximately body temperature saline into the hemodialysis blood circuit for the CO measurement. The syringe warmer provides the user a convenient source of the 30cc injection of approximately body temperature saline for this injection.

In accordance with section 510(k) for the Federal Food, Drug, and Cosmetic Act, Transonic Systems Inc. intends to introduce into interstate commerce the Transonic Syringe Warmer as an accessory to our HD01-CO Hemodialysis Monitor for Cardiac Output (K980906). The HD01-CO Hemodialysis Monitor for Cardiac Output requires the user to make a 30cc injection of ~ body temperature saline into the hemodialysis blood circuit for the CO measurement. The syringe warmer will provide the user a convenient source of the 30cc injection of ~ body temperature saline for this injection.

The provided text describes a 510(k) premarket notification for the Transonic Syringe Warmer, an accessory device. The information primarily focuses on establishing substantial equivalence to a predicate device and does not contain detailed acceptance criteria, specific study designs, or performance metrics in a quantitative manner typical for AI/ML device submissions.

Based on the provided text, I can infer the general nature of the "acceptance criteria" and "study" as implied by the FDA's "Safety and Effectiveness" section, but there are no specific numerical thresholds or detailed study results. The submission relies on demonstrating the device is similar to a predicate device and meets general quality control requirements.

Here's an analysis based on the provided text, attempting to address your points as much as possible, while noting where information is absent for this type of submission:

Acceptance Criteria and Study for the Transonic Syringe Warmer

The Transonic Syringe Warmer is an accessory device intended to warm a 30cc syringe of saline to approximately body temperature for injection into a hemodialysis blood circuit. The acceptance criteria and supporting "study" described are focused on demonstrating the device's safety and effectiveness and its substantial equivalence to a predicate device, rather than a quantifiable performance study against specific metrics for AI/ML devices.

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

Based on the document, the acceptance criteria are implicitly tied to "release specifications" and "product design specifications." The "reported device performance" is essentially that the device functions as intended to warm saline.

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

The document does not specify a "test set" in the context of a typical AI/ML study with data points. Instead, it refers to "all finished products" being tested.

• Sample Size: "All finished products" are tested. This implies a 100% inspection or testing of manufactured units.
• Data Provenance: Not applicable in the context of clinical data. The tests are bench tests performed by the manufacturer, Transonic Systems Inc. (located in Ithaca, NY, USA).
• Retrospective/Prospective: Not applicable. These are manufacturing quality control tests, not clinical studies.

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

This information is not applicable to the type of device and submission described. There is no mention of experts establishing ground truth for a test set, as this is a hardware device undergoing manufacturing quality control rather than a diagnostic AI/ML algorithm.

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

Not applicable. There is no "test set" requiring adjudication in the context of clinical or diagnostic performance. The testing described is internal quality control.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is relevant for diagnostic imaging AI/ML devices, not for a syringe warmer. There is no AI component mentioned in this device.

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

No, a standalone algorithm-only performance study was not done. This device is a hardware accessory and does not contain an AI algorithm.

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

The "ground truth" for this device's performance is based on its adherence to manufacturing specifications and its ability to perform its stated function (warming saline to ~body temperature). This would be validated through engineering measurements and quality control checks, not expert consensus, pathology, or outcomes data in a clinical sense.

8. The sample size for the training set

Not applicable. This device does not use machine learning or have a "training set."

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

Not applicable. This device does not use machine learning or have a "training set."

Summary of the "Study" Described:

The "study" demonstrating the Transonic Syringe Warmer meets its acceptance criteria is best described as a comprehensive quality control and manufacturing testing process rather than a clinical trial or AI performance study.

• Process: "All finished products are tested and must meet all required release specifications before distribution."
• Testing Types: "physical testing, visual examination (in process and finished product)."
• Methodology: "The physical testing is defined by Quality Control Test Procedure documents. These tests are established testing procedures that ensure the products performance parameters conform to the product design specifications."
• Documentation: "The testing instruction records for each of the individually required procedures are approved, released, distributed and revised in accordance with document control cGMP's."

The submission primarily relies on demonstrating substantial equivalence to a predicate device (Vista Dental Anesthetic Syringe Warmer) and asserting that the modified intended use (warming saline for hemodialysis monitoring) "does not raise any new issues of safety or effectiveness." This approach underscores that the primary evidence for this particular 510(k) submission is related to manufacturing quality and equivalence, not advanced performance metrics or clinical study data.

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