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The Cold Compression Wrap Pro is indicated for the temporary relief of minor muscle aches and pains.

The device is indicated for temporary increase in circulation of the treated areas in people who are in good health, and simulates kneading and stroking of tissues using an inflatable wrap.

The cold pack is indicated for localized therapy in situations where cold temperature therapy is necessary or desirable.

Cold Compression Wrap Pro Device consists of a main unit and wraps which simulates kneading and stroking of tissue with the hands by use of inflatable pressure wraps. It is for temporary increase in circulation of the treated areas and temporary relief of minor muscle aches and pains. By inflating the air chambers and then deflating as one cycle, the pressure can be adjusted to avoid any discomfort to the patient.

There are 4 different wraps for Shoulder, elbow/arm, low limbs (Calf and upper leg), ankle/foot. The wrap contains cold pack, and the cold pack is indicated for localized therapy in situations where cold temperature therapy is necessary or desirable.

When the device is used for 30 minutes (Default time) at the operature (5°C~ 40°C) specified in the user manual, the temperature of the cold pack is (-18°C ~-4℃) (±2℃).

The provided document, K230524, is a 510(k) Premarket Notification for a medical device called "Cold Compression Wrap Pro". This document, issued by the FDA, determines the substantial equivalence of the new device to existing legally marketed predicate devices.

Based on the provided document, here's a breakdown of the acceptance criteria and the study proving the device meets them:

Crucially, this is not a clinical study report for an AI/ML medical device. It's a 510(k) submission for a physical medical device (cold compression wrap) with embedded software. Therefore, many of the typical acceptance criteria and study details for AI/ML performance (like MRMC studies, ground truth establishment by experts, and large training/test sets of medical data) are not applicable or present in this document.

The "study" here refers to the non-clinical performance testing conducted to demonstrate that the device functions as intended and is safe.

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

The document does not present a formal table of "acceptance criteria" with quantitative performance metrics for the device's therapeutic effect (e.g., pain reduction scores, circulation improvement). Instead, it focuses on the device's technical specifications and safety parameters, which serve as its de facto acceptance criteria for this type of device.

Here's an attempt to extract the closest equivalents to acceptance criteria and reported performance based on the "510(k) Summary" section, particularly the "Substantial Equivalence Comparison" table and "Summary of Non-clinical Performance Testing":

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

• Sample Size for Test Set: The document describes non-clinical performance testing (electrical safety, EMC, software validation, product appearance/size, wrap performance, function tests). These tests are typically performed on a small sample of manufactured devices (e.g., a few units, or internal test benches). The specific number is not specified in this summary.
• Data Provenance: The tests are "non-clinical" and likely conducted in a lab setting by the manufacturer, Chengdu Cryo-Push Medical Technology Co.,Ltd, which is located in China. The data is prospective, in the sense that the company specifically conducted these tests for the 510(k) submission.

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)

This is a physical device, not an AI/ML diagnostic algorithm. Therefore, the concept of "experts establishing ground truth" in the clinical sense (e.g., radiologists interpreting images) is not applicable. The "ground truth" for this device's performance is established by engineering measurements and adherence to recognized standards (e.g., IEC 60601-1 for electrical safety). The qualifications of the testing personnel are not disclosed in this summary.

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

This is not applicable as there is no human review or adjudication of data points in the way it would occur for AI/ML diagnostic algorithms (e.g., reviewing medical images). The testing involves objective measurements against predefined engineering specifications.

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 MRMC comparative effectiveness study was not done. This type of study is relevant for AI/ML diagnostic tools that assist human readers (e.g., radiologists). This product is a physical therapeutic device, not a diagnostic AI.

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

Not applicable in the context of an AI/ML algorithm. The device has "embedded software" which underwent "software validation". This is a test of the embedded software's functionality and safety (e.g., reliably controlling pressure and time settings), not a standalone diagnostic AI performance evaluation. The device itself operates "as is" when used by a patient, meaning the "algorithm" (software) operates in a standalone manner as part of the device's function, but not as a diagnostic tool.

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

The "ground truth" for this device's performance is based on engineering specifications, recognized international safety standards (e.g., IEC 60601 series), and the functional requirements for a cold compression system. For instance, the ground truth for electrical safety is whether the device actually meets the voltage leakage limits of IEC 60601-1. For the cold pack temperature, it's whether the internal temperature sensor actually registers within the specified range after 30 minutes of operation. This is determined by instrumental measurements and adherence to test protocols, not clinical outcomes or expert consensus on medical data.

8. The sample size for the training set

The device is a physical product with embedded software. It is not an AI/ML device that requires a "training set" of data in the common sense (e.g., images for deep learning). The software is likely programmed deterministically or based on engineered control logic, not "trained" on data. Therefore, the concept of a "training set sample size" is not applicable.

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

Since there is no "training set" in the AI/ML sense, this question is not applicable. The "ground truth" for the device's design and software functionality is rooted in engineering requirements, safety standards, and the intended physiological effects of cold compression therapy (which are well-established prior to this device's development).

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