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The Thermo-STAT is designed to non-invasively treat hypothermic patients by rewarming their body core. This is accomplished with local application of negative pressure and a thermal load (heat) to a distal appendage.

The Thermo-STAT is a non-invasive and portable body core warming device which provides a noninvasive technique to treat and prevent hypothermia. The Thermo-STAT's principle of action for counteracting hypothermia is to create a thermal pipeline between the skin and the body core. The Thermo-STAT functions by applying a combination of heat and pressure to only the distal aspect of an arm or leg. Using the Thermo-STAT, a thermal load is exchanged between the application site and the body core. Vasoconstriction in a hypothermic individual prevents superficial heat alone from effectively altering the body core temperature. The Thermo-STAT circumvents this "vasoconstrictive blockade" with a slight negative pressure (40-60mmHg) and enables a thermal load to be transferred directly and exclusively from a thermal heat pad to the body core via the bloodstream. Current means, such as forced air rewarming, fail to effectively overcome the "vasoconstrictive blockade."

The provided text describes the Thermo-STAT System, a device designed to non-invasively treat hypothermic patients by rewarming their body core.

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

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided text, the primary clinical acceptance criterion is the ability to raise core body temperature in hypothermic patients.

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

• Sample Size: 22 patients
• Data Provenance: The study appears to be prospective as it involved "randomly performed on 22 patients undergoing a variety of general surgical procedures." The country of origin is not explicitly stated but can be inferred to be the USA, given the FDA 510(k) submission.

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

The text does not specify the number of experts or their qualifications for establishing ground truth in the clinical study. The "ground truth" here seems to be the direct physiological measurement of body core temperature change and observation of side effects, likely performed by medical professionals involved in patient care (e.g., anesthesiologists, nurses in the PACU), rather than a panel of independent experts for review.

4. Adjudication Method for the Test Set

The text does not describe an adjudication method for the clinical test set. The outcome measured (2°C rise in body core temperature) is a direct physiological measurement, not an interpretative one that would typically require adjudication. Observational data on side effects would likely be recorded by treating clinicians.

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

The text does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done. The study focuses on the device's direct effect on patients, not on how human readers/clinicians perform with or without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study was done

This question is not applicable to the Thermo-STAT System. The device is a physical medical device designed to directly administer therapy, not an algorithm or AI system for diagnostic or interpretive purposes. Its performance is inherent in its operation on a patient, not a separate standalone algorithmic output.

7. The Type of Ground Truth Used

The ground truth used for the clinical evaluation was physiological measurements (body core temperature) and direct observation of patient outcomes (presence/absence of side effects).

8. The Sample Size for the Training Set

The text does not mention a separate "training set" in the context of machine learning or AI models. This device is a physical therapeutic device, and the clinical study primarily serves as a validation or test set for its performance and safety in humans, not for training an algorithm.

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

As there is no mention of a "training set" for an AI or algorithmic component, this question is not applicable. The device's "training" in a broad engineering sense would refer to its design, prototyping, and bench testing phases, where performance parameters were optimized, but not in the context of data-driven machine learning.

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