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510(k) Data Aggregation
(23 days)
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(71 days)
The device is intended for use as a prosthesis for the surgical repair of soft tissue deficiencies using linear surgical staplers. The device can be used to reinforce staple lines during lung resections, abdominal and thoracic wall repairs, gastric banding, muscle flap reinforcement, repairs, vaginal prolanse, maluing il vaginal prolapse, pelvic floor reconstruction, urethral sling and diaphragmatic, femoral, incisional, incisional, inguinal, lumbar, paracolostomy, scrotal, and umbilical hernial, The device may be used with anastomotic staplers and with non-anastomotic staplers.
Biocompatible, expanded polytetrafluoroethylene (ePTFE) in sleeve form. The sleeve is configured for use with commercially available linear surgical stapless
Here's an analysis of the provided text regarding the acceptance criteria and study for the SEAMGUARD™ Staple Line Reinforcement Material:
Acceptance Criteria and Device Performance
| Acceptance Criteria Category | Specific Criteria | Reported Device Performance |
|---|---|---|
| Material Strength | Sufficient to resist staple pull-through | Mean staple failure force: 0.91 kg |
| Sufficient to prevent significant damage to the integrity of the material | Mean material failure force: 2.01 kg | |
| *Conclusion: Staples fail (straighten) before the material fails, indicating the material is stronger than the staple. * | ||
| Biocompatibility | Inert, biocompatible | Composed of inert, biocompatible ePTFE material with a history of safe and effective use. Animal studies show no adverse histologic reactions in lung tissue. |
Study Details
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Sample size used for the test set and the data provenance:
- Sample Size: Not explicitly stated as a numerical count, but it is implied that "tests conducted" involved multiple staples and material samples to derive mean values.
- Data Provenance: The mechanical tests were conducted by the applicant (W.L. Gore and Associates). The country of origin of the data is not specified, but the applicant is based in the USA. The study appears to be a prospective mechanical testing study conducted in a lab setting, not a clinical trial withpatient data.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- This question is not applicable in this context. The "ground truth" for the mechanical performance was established through physical measurements of staple failure force and material failure force, not through expert consensus or interpretation of medical images. For biocompatibility, the ground truth was based on the known properties of ePTFE and observations from animal studies.
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Adjudication method for the test set:
- Not applicable. As noted above, the "ground truth" was determined by direct physical measurement, not by expert review requiring adjudication.
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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. This is not a medical imaging device or an AI-enabled diagnostic tool. Therefore, an MRMC study is not relevant.
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If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Not applicable. This is not an algorithm or an AI device. The device's performance was evaluated through direct mechanical testing in a standalone manner (the material itself was tested).
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- For mechanical performance: Directly measured physical properties (force required to cause staple straightening/failure and material tear/failure).
- For biocompatibility: Established material properties (ePTFE's known biocompatibility) and observations from animal studies (histologic reactions).
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The sample size for the training set:
- Not applicable as this is not an AI/ML device requiring a training set. The "training" for this device would be its design and manufacturing process, optimized through engineering principles.
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How the ground truth for the training set was established:
- Not applicable, as there is no training set for an AI/ML model.
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