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The SRI/Surgical Express SRI LP Drapes are reusable drapes, including back table covers (model numbers 1616, 1822, 3010, 3015, 3020, 3025, 3030, and 6113), mayo stand covers (model numbers 1247C, 3000, 30004, 3006, and 6247A), and drape sheets (model numbers 3870, 3871, 3872, and 3873) that are intended to be used by health care personnel during surgical procedures to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids, and particulate material.

Reusable drapes, including back table covers (model numbers 1616, 1822, 3010, 3015, 3020, 3025, 3030, and 6113), mayo stand covers (model numbers 1247C, 3000, 30004, 3006, and 6247A), and drape sheets (model numbers 3870, 3871, 3872, and 3873).

I am sorry, but the provided text does not contain the detailed information needed to fill out the table and answer all the questions regarding acceptance criteria and a study proving device performance.

The document is a 510(k) clearance letter from the FDA for SRI/Surgical Express Level IV Drapes. It confirms that the device is substantially equivalent to legally marketed predicate devices. However, it does not describe the specific acceptance criteria used in a performance study, nor does it provide details about such a study (sample sizes, expert qualifications, ground truth establishment, etc.). It only mentions that the device is subject to general controls and existing major regulations.

Therefore, I cannot generate the requested table or answer the specific questions about the study that proves the device meets acceptance criteria based only on the provided text.

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The SRI/Surgical Express SRI Level III Gowns are reusable gowns that are intended to be worn by operating room personnel during surgical procedures to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids, and particulate material.

SRI/Surgical Express Level III Gowns are reusable gowns.

My apologies, but the provided text from the FDA 510(k) clearance letter for the "SRI/Surgical Express Level III Gowns" does not contain the specific information required to answer your request regarding acceptance criteria and a study proving device performance in the context of an AI/ML-driven medical device.

This document is a regulatory clearance letter for a physical product (surgical gowns), not a software or AI device. The information typically associated with studies proving AI device performance (like sample sizes, ground truth establishment, expert qualifications, adjudication methods, MRMC studies, standalone performance, training sets, etc.) is not applicable to this type of traditional medical device.

The letter does contain:

• Trade/Device Name: SRI/Surgical Express Level III Gowns
• Regulation Number: 21 CFR 878.4040
• Regulation Name: Surgical Apparel
• Regulatory Class: II
• Product Code: FYA
• Indications for Use: "The SRI/Surgical Express SRI Level III Gowns are reusable gowns that are intended to be worn by operating room personnel during surgical procedures to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids, and particulate material."
• 510(k) Number: K080707

However, it does not provide any data or details about performance studies in the way you've outlined for an AI/ML device. For a surgical gown, "acceptance criteria" would typically relate to physical properties like barrier protection level (e.g., AAMI Level 3), liquid resistance, tensile strength, sterility, etc., which are usually tested according to recognized standards (e.g., ANSI/AAMI PB70, ASTM F1670, ASTM F1671). The letter states that the device is "substantially equivalent" to legally marketed predicate devices, implying that it meets the same performance standards as those predicates, but it does not detail the specific test results or studies within this document.

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The SRI/Surgical Express SRI Level IV Gowns are reusable gowns that arc intended to be worn by operating room personnel during surgical procedures to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids, and particulate material.

SRI/Surgical Express Level IV Gowns

The provided text is a 510(k) premarket notification letter from the FDA regarding a surgical gown (SRI/Surgical Express Level IV Gowns). This document is a regulatory approval letter and does not contain the information requested in your prompt regarding acceptance criteria, study details, or AI performance metrics.

Therefore, I cannot extract the following information from the given text:

• Table of acceptance criteria and reported device performance
• Sample size and data provenance for the test set
• Number and qualifications of experts for ground truth
• Adjudication method
• MRMC comparative effectiveness study details or effect size
• Standalone algorithm performance
• Type of ground truth used
• Sample size for the training set
• Ground truth establishment for the training set

This document is solely focused on the FDA's determination of substantial equivalence for a physical medical device (surgical gowns) based on existing predicate devices and general controls, not on the performance of a software or AI-based device.

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Surgical gowns are devices that are intended to be worn by health care personnel during surgical or other Ourgiour gowns are a vice that and the wearer from transfer of microorganisms, body fluids, and particulate material.

reusable surgical ' gown manufactured with the W.L. Gore ASSIST™ medical fabric in the Critical Zones.

The provided document describes the acceptance criteria and performance data for the SRI/Surgical Express, Inc. SP™ Reusable Surgical Gown, but it does not contain information related to AI or algorithm-driven device performance. The document is a 510(k) premarket notification for a traditional medical device (a surgical gown) and details its physical, biological, and material properties.

Therefore, many of the requested fields (e.g., sample size for test set, data provenance, number of experts for ground truth, adjudication method, MRMC study, standalone algorithm performance, training set sample size, how training set ground truth was established) are not applicable to this document as it does not describe an AI medical device.

Table of Acceptance Criteria and Reported Device Performance:

Information Not Applicable or Not Provided in the Document (due to the nature of the device as a non-AI product):

2. Sample size used for the test set and the data provenance: Not applicable/Not specified for an AI device. The document mentions "125 wash/dry/steam autoclave cycles" for barrier performance and physical properties testing, which describes the conditions and extent of physical testing, not a dataset for an algorithm. Data provenance (country, retrospective/prospective) is not relevant for this type of testing.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth as typically understood for AI devices (e.g., expert annotations on images) is not relevant to testing a surgical gown's physical properties.
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 for AI-assisted diagnostic or decision-making systems.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable in the context of AI. The ground truth for this device is established by objective standardized test methods (e.g., AATCC, ASTM, ISO) measuring physical and biological properties.
8. The sample size for the training set: Not applicable. This device is not an AI algorithm.
9. How the ground truth for the training set was established: Not applicable. This device is not an AI algorithm.

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