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The Sintea Biotech Traumafix System is indicated for use in the following conditions: - Fracture . - Limb lengthening . - Reconstruction . - . Traction

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Here's an analysis of the provided text regarding the Sintea Biotech Traumafix System, structured to address your specific questions.

Based on the provided text, the Sintea Biotech Traumafix System received 510(k) clearance due to its substantial equivalence to a predicate device, the Lima-Lto External Circular Stabilizer (ECS). The documentation does not describe a study that proves the device meets specific acceptance criteria in the manner one might expect for a novel AI device or a new clinical claim. Instead, the acceptance is based on demonstrating equivalence to an already legally marketed device. Therefore, many of your specific questions regarding acceptance criteria and study details (like sample sizes, expert ground truth, MRMC studies, standalone performance, training sets) are not applicable or cannot be answered from this type of regulatory submission.

Acceptance Criteria and Device Performance

Explanation: The "acceptance criteria" here are effectively the criteria for establishing substantial equivalence to the predicate device. The “reported device performance” is the assertion by the manufacturer that their device meets these equivalence criteria.

Study Details and Ground Truth

Given that this is a 510(k) submission based on substantial equivalence to a predicate device, a traditional study to "prove the device meets acceptance criteria" in the sense of a new clinical investigation with detailed performance metrics was not performed for this specific device. Instead, the argument is that because it is essentially the "same device" as the predicate (Lima-Lto External Circular Stabilizer), its safety and effectiveness are established.

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

   • Not applicable for a distinct test set used for clinical performance validation of this specific device. The submission relies on mechanical tests and the established clinical performance of the predicate device. The text does not provide details of any specific clinical test set for the Sintea Biotech Traumafix System.

2. 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. There is no mention of an expert panel establishing ground truth for a test set in the provided summary. Ground truth, in this context, is implicitly derived from the established safety and effectiveness of the predicate device as a legally marketed product.

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

   • Not applicable. No such adjudication method is described as there isn't a described clinical test set requiring such expert review.

4. 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 device is a mechanical bone fixation system, not an AI-assisted diagnostic or therapeutic tool. Therefore, MRMC studies and "human reader improvement with AI" are not relevant.

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

   • Not applicable. This is a hardware medical device, not an algorithm.

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

   • The "ground truth" for demonstrating safety and effectiveness is primarily the established clinical performance and safety record of the predicate device (Lima-Lto External Circular Stabilizer), combined with mechanical test data on the substantial equivalence of the Sintea Biotech Traumafix System's materials and design to the predicate. No new clinical outcomes data for the Sintea Biotech Traumafix System are presented in this summary.

7. The sample size for the training set:

   • Not applicable. As this is a mechanical device, there is no "training set" in the context of machine learning or AI.

8. How the ground truth for the training set was established:

   • Not applicable. See point 7.

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