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The ORLON® Polyamide Nonabsorbable Needle Suture USP (Polyamide filament) is indicated for use in general soft tissue approximation and/or ligation, including use in cardiovascular, ophthalmic and neurological procedures.

The ORLON® NONABSORBABLE SURGICAL SUTURE USP is a polyamide suture. It is a nonabsorbable, sterile, flexible thread that is prepared from long-chain aliphatic polymers Nylon 6 and Nylon 6,6. It meets all the requirements established by the United States Pharmacopoeia for nonabsorbable sutures. It is offered in the monofilament form and is dyed with an appropriate FDA listed color additive. The suture is provided uncoated and with or without a standard needle attached.

The provided text describes a 510(k) premarket notification for a surgical suture and focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a detailed study with specific performance metrics and acceptance criteria in the manner one might find for a diagnostic AI device.

Therefore, many of the requested elements for acceptance criteria and study details cannot be extracted directly from this document. The document confirms that the device does meet acceptance criteria by stating: "After sterilization, the sutures are subjected to physical tests of diameter, tensile strength, needle attachment and sterility. Once all these tests are conducted and the sutures pass these tests, they are released for distribution." However, it does not provide the specific numerical thresholds for these acceptance criteria or the raw results of these tests.

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

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

Note: The document states the device passes these tests but does not provide the specific numerical acceptance thresholds or the measured performance values. It relies on compliance with United States Pharmacopoeia (USP) standards.

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

• Sample Size for Test Set: Not specified. The document states "the sutures are subjected to physical tests" implying a sample is taken from the manufactured batch, but the size is not given.
• Data Provenance: The tests are conducted internally by SRI ORION PHARMACEUTICALS & SUTURES in India, as the sponsor is based in Bangalore, India. The tests are prospective in the sense that they are performed on manufactured products before release.

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)

Not applicable. For a physical device like a surgical suture, "ground truth" is established by direct physical and chemical testing against established standards (USP). There are no human experts "establishing ground truth" in the way a medical image interpretation study would.

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

Not applicable. As described above, performance is assessed via direct physical testing against USP standards, not through human interpretation or adjudication.

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 (suture), not an AI-assisted diagnostic tool.

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)

The "ground truth" for the device's performance is compliance with the United States Pharmacopoeia (USP) standards for nonabsorbable sutures. This includes specific requirements for diameter, tensile strength, needle attachment, and sterility.

8. The sample size for the training set

Not applicable. This is a manufactured physical device. The concept of a "training set" is relevant for machine learning algorithms, not for the quality control of sutures described here.

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

Not applicable (as explained in point 8).

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