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FortrOss Bone Void Filler is indicated for bony voids or gaps that are not intrinsic to the stability of bony structure. These defects may be surgically created osseous defects or defects created from traumatic injury to the bone. The product is indicated to be used in the posterolateral spine in conjunction with bone marrow aspirate and autograft bone. The product provides a bone void filler that resorbs and is replaced with bone during the healing process.

FortrOss Bone Void Filler is a resorbable bone void filler consisting of calcium phosphate in a porcine gelatin carrier provided in granular form. FortrOss Bone Void Filler is radiopaque, provided sterile and is intended for single use only.

The provided document is a 510(k) summary for a medical device (FortrOss Bone Void Filler) and an FDA clearance letter. It describes a modification to an existing device, not a new device requiring extensive clinical trials for performance validation. Therefore, many of the typical acceptance criteria and study components you'd expect for medical imaging AI or diagnostic devices are not present.

The "performance testing" section explicitly states: "Non-clinical evaluation consisting of material specifications and engineering design review and validation relevant to the line extensions described above were performed." This indicates that the validation focused on comparing the modified device's characteristics (new size, packaging, accessory) to the predicate device, rather than on proving clinical performance or diagnostic accuracy.

Given this context, here's how the requested information maps to the provided text:

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

Based on the document, there are no specific quantitative acceptance criteria or reported device performance metrics in the typical sense for a diagnostic or AI device. The acceptance criterion for this 510(k) Special Modification is Substantial Equivalence to the predicate device.

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

This information is not applicable (N/A) as this was a non-clinical evaluation of a device modification, focusing on material and design validation, not clinical performance with a test set of data.

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)

This information is not applicable (N/A). No ground truth was established by experts for a test set in this 510(k) submission, as it did not involve clinical performance evaluation.

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

This information is not applicable (N/A). No adjudication method was used, as there was no test set requiring ground truth establishment or expert review.

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

This information is not applicable (N/A). This device is a bone void filler, not an AI or diagnostic tool. No MRMC study was conducted.

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

This information is not applicable (N/A). This device is a physical medical implant, not an algorithm or AI.

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

This information is not applicable (N/A). There was no "ground truth" in the diagnostic sense. The "ground truth" for the non-clinical evaluation would have been the established material specifications, engineering design principles, and physical performance testing results confirming the modified device's characteristics met pre-defined requirements, demonstrating equivalence to the predicate.

8. The sample size for the training set

This information is not applicable (N/A). This device is not an AI/ML algorithm that requires a training set.

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

This information is not applicable (N/A). As there is no training set for an AI/ML algorithm, no ground truth was established for it.

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