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The Convertors® Drapes and Leggings are devices intended to be used as a protective patient covering, such as to isolate a site of surgical incision from microbial and other contamination.

These drapes and leggings will be composed of a nonwoven fabric adhesively laminated to a synthetic polymer film. The nonwoven contains adhesively bonded polyester fibers. The film is a 1.25 mil finely embossed blended polyethylene construction.

The provided text describes a medical device called "Convertors® Drapes and Leggings" and outlines a summary of testing performed to demonstrate its safety and effectiveness. However, the information available is from a 510(k) summary (from 1996) for a physical medical device (drapes and leggings), not an AI/ML powered device. Therefore, many of the requested categories related to AI/ML device testing (like sample size for test set, data provenance, ground truth establishment, MRMC studies, standalone performance, training set details) are not applicable or cannot be extracted from this document.

Here's an attempt to answer the questions based only on the provided text, indicating where information is not available or not applicable:

Acceptance Criteria and Study for Convertors® Drapes and Leggings

This document describes the testing for a physical medical device (drapes and leggings), not an AI/ML powered device. Therefore, many of the typical acceptance criteria and study details for AI/ML devices, such as specific performance metrics like sensitivity/specificity, sample sizes for AI model validation, ground truth establishment by experts, or MRMC studies, are not present. Instead, the focus is on material properties, biological safety, and barrier performance.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size: Not specified. The document states that "All materials used in the fabrication... were evaluated," implying testing was done on batches of material or representative samples, but specific numbers are not provided.
• Data provenance: Not applicable in the AI/ML sense (e.g., country of origin of patient data). The data relates to laboratory testing of manufactured materials. The studies are implicitly prospective in that the performance of the materials was tested to ensure they met standards before market introduction.

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

• Number of experts: Not applicable. Ground truth for material properties and biological safety tests is established by standardized testing protocols and measurements, not expert consensus in the diagnostic sense.
• Qualifications of experts: Not applicable. The testing would have been conducted by trained laboratory personnel/technicians, but not "experts" in the context of clinical interpretation or diagnosis.

4. Adjudication method for the test set

• Adjudication method: Not applicable. The tests performed are objective measurements against established standards, not subjective interpretations requiring adjudication.

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

• MRMC study: No. This is not an AI/ML device; therefore, no MRMC study or assessment of human reader improvement with AI assistance was performed.

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

• Standalone study: No. This is not an AI/ML device; therefore, the concept of algorithm-only performance does not apply. The device itself is a standalone physical product.

7. The type of ground truth used

• Type of ground truth: The "ground truth" here is the pass/fail outcome of standardized laboratory and biological tests as defined by international standards (ISO 10993) andASTM methods. For example, a material either passed the hydrostatic head test or it didn't; it either caused sensitization or it didn't.

8. The sample size for the training set

• Sample size for training set: Not applicable. This is not an AI/ML device that requires a training set.

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

• Ground truth for training set: Not applicable. This is not an AI/ML device that requires a training set.

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