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The JAWS™ Nitinol Staple System implants are indicated for use in osteotomy, arthrodesis and fragment fixation of bones and joint of the foot including fixation of small bone fragments (i.e. small fragments of bone which are not comminuted to the extent to preclude staple placement) located in the long bones of the lower extremities such as the fibula and tibia.

The JAWS™ Nitinol Staple System includes three styles of bone staples having various sizes to accommodate a variety of small bone applications. The implants and instruments are sold sterile.

This document is a 510(k) premarket notification for the JAWS™ Nitinol Staple System. It focuses on demonstrating substantial equivalence to predicate devices, rather than providing a detailed study proving the device meets specific acceptance criteria in the context of diagnostic or AI-driven performance.

Therefore, many of the requested details about acceptance criteria, study sample sizes, expert involvement, and ground truth establishment are not applicable to this type of regulatory submission, as it's for a physical medical device (bone staple) and not a diagnostic algorithm or AI.

However, I can extract the information that is present:

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

The document does not explicitly state "acceptance criteria" in a quantitative, pass/fail manner with specific thresholds related to a performance study for the device's clinical efficacy or diagnostic accuracy. Instead, it refers to performance data from mechanical testing.

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

This information is not provided in the document. The document refers to mechanical testing standards (ASTM F564, ASTM F2129) but does not detail the number of samples tested or the specifics of the test setup (e.g., country of origin, retrospective/prospective, which are not relevant for mechanical device testing).

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/not provided. The device is a bone staple. "Ground truth" in the context of expert consensus, pathology, or outcomes data is typically relevant for diagnostic devices or AI, not for the mechanical performance of a surgical implant. The "ground truth" for mechanical testing would be the physical properties measured in a lab according to established engineering standards.

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

This information is not applicable/not provided. Adjudication methods are typically for subjective or clinical assessments, not for objective mechanical testing.

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 information is not applicable/not provided. This is a physical surgical implant, not a diagnostic or AI-assisted device for human readers.

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

This information is not applicable/not provided. This is a physical surgical implant, not a software algorithm.

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

The "ground truth" for the device's performance is based on mechanical testing results obtained through standardized laboratory protocols (ASTM F564 for mechanical properties and ASTM F2129 for corrosion resistance). It's not based on expert consensus, pathology, or outcomes data in the clinical sense mentioned.

8. The sample size for the training set

This information is not applicable/not provided. This refers to a physical device, not an AI or machine learning model that would require a training set.

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

This information is not applicable/not provided. As above, there is no training set for a mechanical device.

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