Search Results

The SMV Scientific Schanz Screws are intended for use as external fixation for fracture fixation (open and closed); pseudoarthrosis or non-union of long bones; limb lengthening by epiphyseal or metaphyseal distraction; correction of bony or soft tissue deformity; correction of segmental bony or soft tissue defects; and joint arthrodesis.

SMV Scientific Schanz Screws are manufactured from medical grade stainless steel. SMV Scientific Schanz Screws are offered in various diameters and lengths. SMV Scientific Schanz Screws are intended to provide fracture fixation.

This document is a 510(k) premarket notification for the "SMV Scientific Schanz Screws." It primarily focuses on demonstrating substantial equivalence to a predicate device based on mechanical performance rather than AI/algorithm performance. Therefore, many of the requested elements for an AI device study are not applicable or cannot be found in this document.

However, I can extract the information relevant to the device's performance testing and acceptance criteria as presented.

Here's an attempt to answer your request based only on the provided text, acknowledging that much of the information you asked for is not present for this type of device submission:

1. Table of Acceptance Criteria and the Reported Device Performance

For this specific device (Schanz Screws), the "acceptance criteria" and "reported device performance" are based on mechanical testing and comparison to a predicate device rather than diagnostic accuracy or AI model metrics.

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

This document describes non-clinical mechanical testing of physical devices. It does not involve a "test set" of patient data or clinical samples in the way an AI diagnostic device would. Therefore:

• Sample Size: Not specified for testing individual screws, but implied to be sufficient for ASTM F1541-02 standards.
• Data Provenance: Not applicable in the context of patient data; the data comes from mechanical testing performed on manufactured Schanz screws.

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. Ground truth, in the AI context, refers to a verified diagnosis or condition. For this mechanical device, "ground truth" relates to physical properties, established through standardized testing (ASTM F1541-02), not by expert human interpretation.

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

Not applicable. This concept pertains to resolving discrepancies in expert interpretations for establishing ground truth in clinical/diagnostic studies, which is not relevant for mechanical device 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

Not applicable. This device is a bone fixation fastener, not an AI-assisted diagnostic tool. No human reader studies are mentioned or relevant to its 510(k) submission for mechanical equivalence.

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

Not applicable. There is no algorithm or AI component in the SMV Scientific Schanz Screws as described in this document. The "standalone performance" refers to the mechanical properties of the screws themselves.

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

The "ground truth" for this device's performance is established via standardized mechanical testing results (Static Four-point Bending and Static Torsion per ASTM F1541-02). The "ground truth" for equivalence is a comparison to the mechanical performance characteristics of the predicate device (Synthes Self-Drilling Schanz Screws, K040701).

8. The sample size for the training set

Not applicable. This device does not use machine learning or AI, so there is no training set.

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

Not applicable. As there is no training set for an AI model, this question is not relevant.

Ask a specific question about this device