Search Results

The GORE SEAMGUARD Staple Line Reinforcement Material for Circular Staplers is indicated for use in surgical procedures in which a soft tissue anastomosis with staple line reinforcement is needed. GORE SEAMGUARD® Bioabsorbable Staple Line Reinforcement Material can be used for reinforcement of staple lines during bariatric, colon, colorectal, gastric, and small bowel procedures.

The GORE SEAMGUARD® Bioabsorbable Staple Line Reinforcement Material for circular staplers consists of a bioabsorbable membrane comprosed of a microporous structure of synthetic bioabsorbable glycolide and trimethylene carbonate copolymer. The GORE SEAMGUARD® Bioabsorbable Staple Line Reinforcement Material is to be used with surgical stapling devices.

The GORE SFAMGUARD® Bioabsorbable Staple Line Reinforcement Material is supplied in sterile polyethylene terphthalate / polyethylenc pouches. These pouches contain the necessary material for the cartridge jaw and the anvil jaw of the surgical stapler.

The provided text is a Premarket Notification 510(k) Summary for the GORE SEAMGUARD® Staple Line Reinforcement Material. This type of document is filed with the FDA to demonstrate that a new medical device is substantially equivalent to a legally marketed predicate device.

It's important to understand that a 510(k) submission primarily focuses on substantial equivalence to an existing device, rather than proving that the device "meets acceptance criteria" in the way one might describe the performance of software or diagnostic AI. The "acceptance criteria" here largely revolve around demonstrating that the new device performs at least as well as and is chemically and physically equivalent to its predicate.

Therefore, many of the specific questions about acceptance criteria, sample sizes for test sets, expert ground truth, MRMC studies, standalone performance, and training sets – which are typical for AI/software device evaluation – are not applicable to this type of medical device submission. This submission is for a material (staple line reinforcement) and not a software or AI device.

However, I can extract the relevant information from the provided text in the spirit of your request:

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

For a material device like the GORE SEAMGUARD®, "acceptance criteria" are generally understood as demonstrating equivalence to the predicate device in terms of design, materials, and functional attributes. The document does not specify quantitative performance metrics as "acceptance criteria" in a table format, but rather relies on a general statement of substantial equivalence.

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

The document does not specify sample sizes for any "test set" or data provenance (e.g., country of origin, retrospective/prospective). Substantial equivalence for this type of device is typically established through a combination of engineering tests (e.g., material testing, mechanical strength), biocompatibility testing, and comparison of design features, rather than a clinical "test set" in the sense of patient data.

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

Not Applicable. This is a material device, not a diagnostic or AI device that relies on expert ground truth for performance evaluation in a "test set."

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

Not Applicable. See point 3.

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 a physical medical device, not an AI or software device. No MRMC study was mentioned or would be relevant.

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

Not Applicable. This is a physical medical device, not an algorithm.

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

Not Applicable. See point 3. The "ground truth" for a device like this would be established through established engineering standards for material properties, biocompatibility, and functional performance in relevant models or in vitro tests. Clinical outcomes would be observed over time post-market, but aren't typically part of the 510(k) "ground truth" for demonstrating substantial equivalence.

8. The sample size for the training set

Not Applicable. This is a physical medical device, not a machine learning model. There is no concept of a "training set."

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

Not Applicable. See point 8.

Ask a specific question about this device