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The Skeletal Dynamics REDUCT® Headless Compression Screw System is intended for fixation of osseous fragments or fractures, arthrodesis of small joints, and osteotomies, with the appropriately sized screw.

The REDUCT® Headless Compression Screw (HCS) System consists of the following screws from medical grade Titanium Alloy (ASTM F136).

• · 2.5mm cannulated HCS screws: 10mm 30mm
• 3.5mm cannulated HCS screws: 10mm 50mm
• 4.5mm cannulated HCS screws: 20mm 65mm
• · 2.0mm non-cannulated HCS Arthrodesis screws: 20mm 34mm
• 2.5mm cannulated HCS Arthrodesis screws: 26mm 40mm
• 3.5mm cannulated HCS Arthrodesis screws: 32mm 46mm
  The REDUCT® Headless Compression Screw includes appropriate instrumentation, as identified in the surgical technique.

The provided text describes the 510(k) premarket notification for the REDUCT® Headless Compression Screw System. This document details the device's technical specifications and claims of substantial equivalence to a predicate device, rather than providing a study on an AI/ML powered device. Therefore, most of the requested information regarding acceptance criteria, study design, and performance metrics related to AI/ML devices is not present in the provided text.

However, I can extract the relevant information concerning the device's performance testing and acceptance criteria as described for this non-AI medical device.

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

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 does not specify the exact sample sizes for each mechanical test (e.g., number of screws tested for each parameter). The tests were performance-based, carried out in accordance with established ASTM standards. This type of testing is generally performed in a laboratory setting, not with patient data. Therefore, the concepts of "data provenance," "country of origin," "retrospective," or "prospective" do not apply in the context of this 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 question is not applicable. The "ground truth" for this device is based on objective mechanical properties measured by instruments and adherence to established ASTM standards, not on expert interpretations of data.

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

This question is not applicable. Adjudication methods are relevant for studies involving human interpretation or subjective assessments, which is not the case for the mechanical testing of a bone screw system.

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 question is not applicable. An MRMC study is relevant for AI/ML powered devices that interact with human interpretation of medical images or data. The REDUCT® Headless Compression Screw System is a physical medical device, not an AI/ML system.

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

This question is not applicable. The device is not an algorithm.

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

The ground truth for this device's performance testing is based on:

• Mechanical properties: Measured values (e.g., force, torque, strength) obtained through standardized laboratory testing.
• Adherence to standards: Compliance with the specifications and methodologies outlined in ASTM F543 and ASTM F2193.
• Substantial equivalence: Comparison of these mechanical properties and the device's design/materials to a legally marketed predicate device (K143624).

8. The sample size for the training set

This question is not applicable. There is no AI/ML model for which to define a training set.

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

This question is not applicable. There is no AI/ML model, and thus no training set or ground truth for a training set.

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