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• Bone fracture fixation; .
• . Osteotomy;
• . Arthodesis;
• . Correction of deformity;
• Revision procedures where other treatments or devices have been unsuccessful; .
• . Bone reconstruction procedures.

This submission is a line extension to the Hoffmann® II External Fixation System. The components of the Hoffmann® Light System will be based on the component designs of the Hoffmann® II External Fixation System.

The Hoffmann® Light System is an external fixation system that is a line extension to the Hoffmann® II External Fixation System. The device is indicated for use in bone fracture fixation, osteotomy, arthrodesis, correction of deformity, revision procedures where other treatments or devices have been unsuccessful, and bone reconstruction procedures.

The acceptance criteria for the Hoffmann® Light System are based on its substantial equivalence to the Hoffmann® II External Fixation System in terms of intended use, design, materials, and operational principles. The study that proves the device meets the acceptance criteria demonstrates that the subject components of the Hoffmann® Light System are substantially equivalent in strength to the predicate components of the Hoffmann® II External Fixation System.

Here's the detailed breakdown of the information requested:

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

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

The provided document does not specify the exact sample size used for the strength testing or the data provenance (e.g., country of origin, retrospective/prospective). It only states that "The test results demonstrate that the subject components are substantially equivalent in strength to the predicate components."

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

This information is not provided in the document. The acceptance criteria are based on direct physical properties (strength) and design/material equivalence, rather than a clinical ground truth established by medical experts for a diagnostic or AI device.

4. Adjudication method for the test set

This information is not applicable and not provided in the document. Adjudication methods like 2+1 or 3+1 are typically used for clinical evaluations involving human interpretation (e.g., image reading) where ground truth might be ambiguous and require expert consensus. The testing here appears to be mechanical/physical.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is relevant for diagnostic devices (especially those using AI) where human readers interpret medical images or data. The Hoffmann® Light System is an external fixation device, not a diagnostic tool, and does not involve human readers interpreting data for diagnosis.

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

A standalone performance study, as understood for AI algorithms, was not done. The device is a mechanical orthopedic device, not an AI algorithm. Its performance is measured through physical and mechanical testing, not algorithmic accuracy.

7. The type of ground truth used

The "ground truth" for this device's acceptance is based on mechanical testing results (demonstrating equivalent strength) and engineering assessment of design, materials, and operational principles against a legally marketed predicate device. This is a comparison against established engineering standards and the performance of an existing, approved device, rather than a clinical ground truth from pathology or patient outcomes.

8. The sample size for the training set

This information is not applicable and not provided. The Hoffmann® Light System is a physical medical device, not an AI model that requires a training set.

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

This information is not applicable. As stated above, the device is not an AI model with a training set.

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