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Skin cooling in conjunction with drinatological laser treatments to reduce pain and thermal damage to skin tissue.

The C300 Chiller System is a thermoelectric cooler (TECs) which utilizes the Peltier effect. The chiller employs solid-state heat pumps that cool thermoelectrically and contain no moving parts. The C300 Chiller System is contained within a single housing. The chiller console is electrically connected to a standard 110 VAC wall outlet. The unit delivers low temperature air at an adjustable rate through a treatment tube. The user interacts with the C300 Chiller System by turning the power switch to the on position and selecting the desired temperature via the display on the digital controller

The provided document is a 510(k) summary for the Reliant Technologies C300 Chiller System. It details the device, its intended use, and a comparison to predicate devices to establish substantial equivalence. However, it does not contain a detailed study with acceptance criteria and reported device performance in the format typically used for medical AI device submissions.

This document describes a physical medical device (a chiller system) rather than an AI/ML-driven diagnostic or prognostic device. Therefore, many of the requested categories for AI/ML device studies (such as sample size for test sets, data provenance, number of experts for ground truth, adjudication method, MRMC studies, standalone performance, training set size, and ground truth for training) are not applicable to the information presented in this 510(k) summary.

The closest information related to "acceptance criteria" and "device performance" in the context of this document is the claim of "Clinical Performance Data" and "Technological Characteristics" for establishing substantial equivalence.

Here's an attempt to answer the request based only on the provided text, acknowledging that much of the requested information for AI/ML devices is not present:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance
Not applicable. This is a physical device, and the evaluation for substantial equivalence does not involve a "test set" in the context of AI/ML performance evaluation studies. The document mentions "Sufficient safety data has been gathered," but does not specify the sample size or provenance of that data beyond it being part of the clinical performance data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. This document does not describe an AI/ML device requiring ground truth establishment by experts for a test set.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
Not applicable.

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 is not an AI/ML diagnostic or prognostic device designed to assist human readers.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
Not applicable. This is a physical chiller system, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable in the AI/ML sense. The "ground truth" for this device's performance would be engineering specifications (e.g., actual cooling capacity vs. designed cooling capacity) and clinical safety outcomes. The document broadly refers to "sufficient safety data" and "performs as clinically intended," but doesn't detail how this "ground truth" was established or measured.

8. The sample size for the training set
Not applicable. This is a physical device, not an AI/ML algorithm requiring a training set.

9. How the ground truth for the training set was established
Not applicable.

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