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The AERO CHROME* Breathable Performance Surgical Gowns are surgical apparel intended to be worn by healthcare professionals to help protect both the patient and the healthcare worker from the transfer of microorganisms, body fluids, and particulate matter. The AERO CHROME* Breathable Performance Surgical Gowns meet the Level 4 AAMI PB70:2012 Liquid Barrier classifications.

The AERO CHROME* Breathable Performance Surgical Gowns have a SMS/F/SMS design (Spunbond-Meltblown- Spunbond/Film/Spunbond-Meltblown-Spunbond) that provides a PB70:2012 Level 4 Liquid Barrier Performance Barrier in the critical zone. The back of the gown has a Spundbond-Meltblown- Spunbond (SMS) fabric which allows for air-breathability with an AAMI Level 1 Liquid Barrier Performance Barrier. The AERO CHROME* Breathable Performance Surgical Gown is a single use gown, supplied sterile (via Ethylene Oxide) or as bulk non-sterile product. The gowns come in the following various sizes: Small, Large, X-Large, XX-Large, XXX-Large, X-Long XL, X-Long XXL.

This document is a 510(k) Premarket Notification for the AERO CHROME* Breathable Performance Surgical Gowns, seeking substantial equivalence to a predicate device (K153255). It is not a study proving device meets acceptance criteria in the context of an AI/ML medical device. Therefore, it does not contain the information required to answer the requested questions regarding acceptance criteria, study design, sample sizes, expert involvement, or training data for an AI/ML device.

This document describes the physical and performance characteristics of a surgical gown and compares it to a previously cleared predicate device to demonstrate substantial equivalence for regulatory purposes. The "acceptance criteria" referred to in the document are the performance standards (e.g., AAMI PB70:2012 liquid barrier classifications, ISO standards for biocompatibility, flammability) that the surgical gown must meet. The "study" proving device meets acceptance criteria refers to the non-clinical testing performed on the gowns to ensure they comply with these standards.

Here's an attempt to answer the questions based on the provided document, understanding that the questions are framed for AI/ML devices and this document is for a physical medical device (surgical gown):

Acceptance Criteria and Device Performance (for a physical medical device - surgical gown)

The document primarily focuses on demonstrating that the subject device (AERO CHROME* Breathable Performance Surgical Gowns) is substantially equivalent to a predicate device (K153255) by meeting the same regulatory and performance standards. The "acceptance criteria" here are established industry standards and regulatory requirements for surgical gowns. The "reported device performance" is a statement of compliance with these standards based on non-clinical testing.

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

Based on the "Technological Characteristic Comparison Table" and "Summary of Non-Clinical Testing" sections:

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

The document does not specify the exact sample sizes used for each material or performance test. It states "non-clinical tests" were conducted. The data provenance is industrial testing of material and product performance, typically conducted in a laboratory setting by the manufacturer or a third-party testing facility. This is a prospective testing regimen to ensure product quality and regulatory compliance. Country of origin for data is not specified but would be the location of the testing laboratories.

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

This question is not applicable to this type of device. "Ground truth" in this context is established by standardized test methods (e.g., AATCC, ASTM, ISO standards) and the results obtained from calibrated scientific instruments. There are no human "experts" establishing ground truth in the sense of medical diagnosis or image annotation for this product testing. The "experts" involved would be qualified laboratory technicians and engineers proficient in performing the specified material and performance tests.

4. Adjudication method for the test set

Not applicable. This is not an AI/ML or diagnostic study requiring adjudication of expert opinions. Results are binary (Pass/Fail) based on meeting pre-defined thresholds in the specified standards.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done

Not applicable. This is a physical medical device (surgical gown), not a diagnostic tool or AI/ML system, so MRMC studies are not relevant. This document is a comparison of the subject device to a predicate device based on material composition and physical performance, not a comparative effectiveness study involving human readers.

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

Not applicable. This is not an algorithm. The "standalone" performance here refers to the gown's intrinsic physical and barrier properties as measured in the laboratory tests.

7. The type of ground truth used

The "ground truth" for this device is based on standardized physical and chemical test results against established performance specifications (e.g., AAMI PB70, ISO 10993, ASTM standards). It is not expert consensus, pathology, or outcomes data in the medical context of AI/ML.

8. The sample size for the training set

Not applicable. Surgical gowns are not "trained" like AI models. The document describes the "construction overview" and materials used, which are the product's design specifications. There isn't a "training set" in the computational sense.

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

Not applicable. As there is no "training set," there is no "ground truth" to establish for it in this context. The manufacturing process and material specifications for the gown are derived from engineering design, material science data, and compliance with the relevant industry standards.

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