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OrthoPediatrics PediLoc™ Tibia Plates are indicated for fractures, osteotornies, and non-unions of the pediatric and small stature adult tibia.

The OrthoPediatrics PediLoc™ Tibial Plates are machined metallic plates that offer screw to plate locking designed for various fracture modes of the distal end of the tibia and other small bones.

The provided submission describes a medical device, the OrthoPediatrics PediLoc™ Tibial Plate System, and its substantial equivalence to previously cleared predicate devices. It does not contain information about a study proving the device meets specific acceptance criteria in the context of AI/ML performance, nor does it include details on clinical studies with defined endpoints, statistical analysis, or ground truth establishment relevant to AI/ML device evaluations.

Therefore, most of the requested information regarding acceptance criteria, study design, sample sizes, expert involvement, and ground truth for an AI/ML device is not applicable or cannot be extracted from this document.

However, based on the document's content, here's what can be inferred about the "acceptance criteria" and "study" in the context of this 510(k) submission:

Acceptance Criteria and Device Performance (in the context of 510(k) for a traditional medical device)

Study Information (in the context of 510(k) for a traditional medical device)

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

• Sample Size: Not applicable. This 510(k) relies on engineering calculations and comparison to predicate devices, not a test set of data in the AI/ML sense. Mechanical testing (implied by "engineering (strength) calculations") would involve a sample of manufactured devices, but the specific number is not provided, and it's not a "test set" of patient data.
• Data Provenance: Not applicable in the context of patient data. The "data" here refers to engineering and material specifications.

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

• Not Applicable. Ground truth, in the AI/ML sense, is not established for patient data in this submission. The "ground truth" for a mechanical device would be the physical properties and performance measured against engineering standards. The FDA reviewers and the manufacturer's engineers are the "experts" involved in assessing this.

4. Adjudication method for the test set

• Not Applicable. There is no "test set" of patient data requiring 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 not an AI/ML device.

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

• Not Applicable. This is not an AI/ML device.

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

• Engineering Specifications and Material Standards: The "ground truth" for this device's performance is its adherence to mechanical engineering principles, material science standards (e.g., ASTM-F138 for 316L stainless steel), and comparison of its design and intended use to legally marketed predicate devices.

8. The sample size for the training set

• Not Applicable. This is not an AI/ML device, so there is no training set.

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

• Not Applicable. This is not an AI/ML device, so there is no training set or associated ground truth establishment.

In summary: This document is a 510(k) premarket notification for a traditional surgical implant. The "study" predominantly refers to engineering calculations and a comparison to known predicate devices to establish substantial equivalence, rather than a clinical trial or AI/ML performance evaluation study against specific acceptance criteria.

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