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The Disposable Powered Articulating Endoscopic Linear Cutter Stapler & Reloads are intended for transection, resection, and/or creation of anastomoses. The instruments have application in multiple open or minimally invasive general abdominal, gynecologic, thoracic, and pediatric surgical procedures.

The Disposable Powered Articulating Endoscopic Linear Cutter Stapler & Reloads are sterile. single patient use instruments that simultaneously cut and staple tissue through a battery powered firing system. The Power Stapler and Reloads are sterilized by irradiation. The instruments deliver six staggered rows of staples, three on either side of the cut line. The instruments are available in three shaft lengths: compact (192±20mm), regular (252±20mm) and long (352±20mm). The shaft can rotate freely in both directions and incorporates an articulation mechanism, which enables the distal portion of the shaft to pivot to facilitate lateral access to the operative site.

This document is a 510(k) Pre-market Notification for a medical device, specifically a "Disposable Powered Articulating Endoscopic Linear Cutter Stapler & Reloads." This type of document is filed with the FDA to demonstrate that a new device is substantially equivalent to a legally marketed predicate device, rather than to prove its safety and effectiveness through a comprehensive clinical study per se.

Therefore, the requested information regarding "acceptance criteria" and "study that proves the device meets the acceptance criteria" in the context of AI/Software device performance is not applicable to this document. This document describes a physical surgical stapler and its reloads, not a software or AI medical device.

The "Performance Data" section (Section 9) describes non-clinical performance tests conducted to demonstrate substantial equivalence to a predicate device, not to establish performance criteria for an AI or software algorithm.

Here's why each of your requested points cannot be answered from this document:

1. A table of acceptance criteria and the reported device performance: The document lists non-clinical tests (e.g., Visual Appearance, Sterility, Hardness, Anastomosis and Cutting Performance) and states, "During verification testing, all data meets pre-defined criteria." It does not provide specific numerical acceptance criteria or reported performance values for these physical device attributes. It also doesn't relate to AI/software performance.
2. Sample sized used for the test set and the data provenance: Not applicable. The tests are for a physical device, not an algorithm's test set. There's no "data provenance" in the sense of clinical data (e.g., country of origin, retrospective/prospective).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/software device is not relevant here.
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-assisted device.
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 concensus, pathology, outcomes data, etc): Not applicable.
8. The sample size for the training set: Not applicable. There is no training set mentioned as this is not an AI/software device.
9. How the ground truth for the training set was established: Not applicable.

In summary, this 510(k) submission focuses on demonstrating the substantial equivalence of a physical surgical stapler to an existing one. It does not involve AI or software, and therefore, the questions about acceptance criteria and study design for such devices are not relevant to the information provided in this document.

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The Disposable Powered Articulating Endosopic Linear Cutter Stapler & Reloads are intended for transection, resection, and/or creation of anastomoses. The instruments have application in multiple open or minimally invasive general abdominal, gynecologic, thoracic, and pediatric surgical procedures.

The Disposable Powered Articulating Endoscopic Linear Cutter Stapler & Reloads are sterile, single patient use instruments that simultaneously cut and staple tissue through a battery powered firing system. The Power Stapler and Reloads are sterilized by irradiation. The instruments deliver six staggered rows of staples, three on either side of the cut line. The instruments are available in three shaft lengths: compact (192±20mm), regular (252±20mm) and long (352±20mm). The shaft can rotate freely in both directions and incorporates an articulation mechanism, which enables the distal portion of the shaft to pivot to facilitate lateral access to the operative site.

This document describes a 510(k) premarket notification for a medical device, the "Disposable Powered Articulating Endoscopic Linear Cutter Stapler & Reloads." The core of a 510(k) submission is to demonstrate that a new device is "substantially equivalent" to a legally marketed predicate device. This process primarily relies on non-clinical performance testing and comparison of technological characteristics, rather than extensive clinical efficacy studies in the same way a PMA (Pre-Market Approval) process would require.

Therefore, the input document does not contain the information requested in your prompt regarding acceptance criteria and studies proving the device meets them in the context of diagnostic accuracy or comparative effectiveness with human readers. This is because the device described is a surgical instrument (a stapler), not a diagnostic AI system.

However, I can extract information related to performance testing that serves as "acceptance criteria" for this type of device to demonstrate substantial equivalence.

Here's the information derived from the document, tailored as much as possible to your prompt's structure, but with the understanding that it's for a surgical stapler and not an AI diagnostic tool:

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

The document lists "Non-Clinical Tests," "Biocompatibility Tests," "Electrical Safety Test," and "EMC Test" that the device "passed." For a 510(k) submission of a surgical stapler, "passing" these tests indicates that the device met predetermined performance specifications (which serve as acceptance criteria) established by the manufacturer and recognized standards. Specific numerical acceptance criteria are generally detailed in the full test reports, which are summarized here.

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

The document does not specify exact sample sizes for each non-clinical test. For devices like this, sample sizes would typically be determined by statistical methods or industry standards for each specific test (e.g., number of stapler firings, number of animals for biocompatibility tests). There is no mention of "data provenance" in terms of country of origin or retrospective/prospective as this is not a diagnostic AI study. The "test set" here refers to the physical devices and materials used in the non-clinical and biocompatibility testing.

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 the provided document. The device is a surgical stapler, not a diagnostic AI system that requires expert interpretation for ground truth establishment. The "ground truth" for stapler performance would be objective measurements (e.g., staple formation integrity, seal strength, cutting effectiveness) against predefined engineering specifications and relevant ISO/ASTM standards.

4. Adjudication method for the test set

This question is not applicable to the provided document. Adjudication methods (like 2+1 or 3+1) are used in diagnostic studies to resolve discrepancies in expert interpretations to establish a ground truth. For a surgical stapler, performance is evaluated against objective, measurable criteria, not subjective interpretation.

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

This question is not applicable to the provided document. An MRMC study is relevant for diagnostic AI tools, not surgical instruments. The device's performance is not about human 'readers' or AI assistance.

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

This question is not applicable to the provided document. This device is a powered surgical instrument; there is no "algorithm only" or "human-in-the-loop" performance in the context of diagnostic assessment. It functions as a tool operated by a surgeon.

7. The type of ground truth used

For this surgical stapler, the "ground truth" is established through:

• Engineering Specifications and Performance Standards: The device's physical dimensions, electrical characteristics, firing mechanism, and output (staple form, cut line) are compared against pre-defined engineering requirements and relevant international standards (e.g., ISO, ASTM, AAMI).
• Biocompatibility Standards: Materials are tested according to ISO 10993 series for their safety in contact with the body.
• Sterility Assurance: Sterilization methods are validated to achieve a specified Sterility Assurance Level (SAL) (10^-6 in this case).
• Functional Testing: "Anastomosis and cutting performance," "resistance to pressure," "sharpness," etc., are assessed through laboratory and potentially ex-vivo tissue testing against established benchmarks for similar devices.

8. The sample size for the training set

This question is not applicable to the provided document. "Training set" refers to data used to train an AI model. This document describes a physical surgical device, not an AI system.

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

This question is not applicable to the provided document, as there is no "training set" for an AI model.

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