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1. Fixation of small bones, including those in the foot, patella, ankle, wrist and elbow.
2. Arthrodesis of the foot, wrist and elbow.
3. Small and long bone osteotomies.
4. Fracture fixation of small bones, small bone fragments and long bones.

The 3.0mm Biomet Cannulated Screw System consists of bone screws of various lengths and are Ti alloy partially threaded screws with the associated 3.0mm washer. They are offered in both a sterile and non-sterile configuration. The 3.0mm Biomet Cannulated Screw System also contains dimensionally optimized corresponding instruments which are used with the 3.0mm Biomet Cannulated Screws and washers to aid in the alignment and stabilization of fractures to the skeletal system.

This document is a 510(k) premarket notification for a medical device called the "3.0mm Biomet Cannulated Screw System." It seeks to demonstrate substantial equivalence to legally marketed predicate devices, not necessarily to prove specific performance criteria against predefined thresholds.

Therefore, the requested information elements related to acceptance criteria and a study proving those criteria are not directly applicable or available in this specific type of regulatory submission document. This document focuses on demonstrating that the new device is as safe and effective as a previously cleared device.

However, I can extract the relevant information from the document as it pertains to the basis for substantial equivalence, which includes some performance data.

Here's a breakdown based on your request, as much as can be derived from the provided text:

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

This document does not specify formal "acceptance criteria" for clinical performance that would typically be seen in a clinical trial. Instead, the performance is compared against predicate devices to demonstrate substantial equivalence. The non-clinical tests indicate that the new device's performance is "equal to or better than the predicate devices."

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 non-clinical tests were conducted per ASTM F543, which specifies testing methodologies, but the sample size used for these specific tests is not provided in this summary.
• Data Provenance: Not specified, but generally, bench testing (non-clinical) occurs in a lab setting, not tied to patient data. Since no clinical studies were performed, there is no patient data provenance to report.

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. This device underwent non-clinical (bench) testing, not clinical studies requiring expert ground truth establishment.

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

• Not applicable. This was bench testing, not a clinical study involving human judgment 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

• No. This is a bone fixation screw system, not an AI/imaging device. Therefore, no MRMC study or AI assistance evaluation was performed.

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

• Not applicable. This is a physical medical device (bone screw), not an algorithm or software.

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

• For the non-clinical tests, the "ground truth" or reference for performance was established by standardized mechanical testing methodologies (ASTM F543) and comparison to the performance of legally marketed predicate devices. There is no clinical ground truth (like pathology or outcomes data) mentioned because no clinical studies were performed.

8. The sample size for the training set

• Not applicable. This is a physical medical device, not a machine learning algorithm that requires a training set.

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

• Not applicable. As above, this is not a machine learning algorithm.

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