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The GYNECARE PROLIFT* Total, Anterior, and Posterior Pelvic Floor Repair Systems are indicated for tissue reinforcement and long-lasting stabilization of fascial structures of the pelvic floor in vaginal wall prolapse where surgical treatment is intended, either as mechanical support or bridging material for the fascial defect.

The GYNECARE PROLIFT+M* Total, Anterior, and Posterior Pelvic Floor Repair Systems, through the placement of GYNECARE GYNEMESH M* Partially Absorbable Mesh, are indicated for tissue reinforcement and long-lasting stabilization of fascial structures of the pelvic floor in vaginal wall prolapse where surgical treatment is intended, either as mechanical support or bridging material for the fascial defect.

The GYNECARE PROLIFT Total, Anterior, and Posterior Pelvic Floor Repair Systems consist of pre-cut non-absorbable mesh implants and a set of instruments to facilitate mesh implant placement.

The GYNECARE PROLIFT+M Total, Anterior, and Posterior Pelvic Floor Repair Systems consist of pre-cut partially absorbable mesh implants and a set of instruments to facilitate mesh implant placement.

The provided text is a 510(k) summary for the GYNECARE PROLIFT and GYNECARE PROLIFT+M Pelvic Floor Repair Systems. It focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance studies with acceptance criteria in the typical sense of a novel algorithm or diagnostic device.

Therefore, the requested information regarding acceptance criteria, device performance tables, sample sizes, expert ground truth, adjudication methods, MRMC studies, standalone performance, training sets, and how ground truth was established for training sets cannot be fully extracted from this document in the way it would for a machine learning or diagnostic device.

Here's an attempt to answer based on the available information:

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

The document does not explicitly state quantitative "acceptance criteria" in terms of specific performance metrics (e.g., accuracy, sensitivity, specificity) with associated thresholds as might be seen for a diagnostic device. Instead, the acceptance is based on demonstrating substantial equivalence to predicate devices. The "performance" assessment is qualitative, focusing on material properties and functional testing, aligning with the "Guidance for the Preparation of a Premarket Notification Application for a Surgical Mesh."

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

The document mentions "bench and cadaver testing" and "preclinical modelling" for functional performance. It does not provide specific sample sizes for these tests, nor does it detail data provenance (e.g., number of cadavers, specifics of bench tests). This is typical for a 510(k) submission focused on mechanical and material properties rather than clinical performance data from patient populations.

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 information is not applicable and hence not provided. The "ground truth" here relates to the material and functional properties meeting engineering specifications and biocompatibility standards, not to interpretations by medical experts as in a diagnostic study.

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

Not applicable. This is not a study requiring expert adjudication of cases.

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 such study was conducted or mentioned, as this is not an AI/diagnostic device.

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

Not applicable. This is not an AI/diagnostic device.

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

The "ground truth" relates to:

• Biocompatibility: Conformance to ISO 10993-1 standards, which involves various biological tests (e.g., cytotoxicity, sensitization, irritation).
• Functional Performance: Meeting established engineering and functional requirements for surgical mesh, likely based on validated test methods and established in FDA guidance.
• Material Composition: Verification of the chemical and physical properties of the polymers to ensure they are identical or equivalent to those in the predicate devices.

8. The sample size for the training set

Not applicable. There is no machine learning "training set" for this type of device.

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

Not applicable. There is no machine learning "training set" for this type of device.

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