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In-Line Connector Growing Rods are indicated in patients under 10 years of age with potential for additional spine growth who require surgical treatment to obtain and maintain correction of severe, progressive, life-threatening, early onset spinal deformities associated with thoracic insufficiency, including early onset scoliosis, as part of a growing rod construct.

CREO® Stabilization System: In-Line Connector Growing Rods consist of rod-to-rod connectors which can be surgically lengthened on a periodic basis as the patient grows. These connectors may be used as part of a growing rod construct consisting of rods, screws, hooks, offset connectors, and cross-connectors, and are limited to a posterior approach. When used as part of a growing rod system, In-Line Connector Growing Rods are intended for use only with CREO® Stabilization System fusion constructs cleared for pediatric use. In-Line Connector Growing Rods are manufactured from titanium alloy.

REVERE® Stabilization System: In-Line Connector Growing Rods consist of rod-to-rod connectors which can be surgically lengthened on a periodic basis as the patient grows. These connectors may be used as part of a growing rod construct consisting of rods, screws, hooks, offset connectors, and cross-connectors, and are limited to a posterior approach. When used as part of a growing rod system, In-Line Connector Growing Rods are intended for use only with REVERE® Stabilization System fusion constructs cleared for pediatric use. In-Line Connector Growing Rods are manufactured from titanium alloy.

The provided documents are a 510(k) summary and FDA clearance letters for the CREO® Stabilization System and REVERE® Stabilization System. These documents pertain to medical devices, specifically stabilization systems for spinal deformities, not AI/ML-driven diagnostic or prognostic tools. Therefore, much of the requested information about acceptance criteria, study design for AI performance, expert adjudication, MRMC studies, standalone performance, training set details, and ground truth establishment, which are typical for AI/ML device evaluations, is not applicable to this submission.

The FDA clearance is based on the substantial equivalence of the devices to legally marketed predicate devices, not on a study proving the device meets specific acceptance criteria in the context of AI/ML performance metrics (like sensitivity, specificity, AUC).

Here's a breakdown of the applicable information:

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

Since this is a substantial equivalence submission for medical devices (spinal stabilization systems) and not an AI/ML diagnostic tool, traditional performance metrics like sensitivity, specificity, or AUC with predefined acceptance criteria are not provided. The "performance" is demonstrated by showing that the technological characteristics and intended use are substantially equivalent to existing, legally marketed predicate devices.

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

Not applicable. This submission is for substantial equivalence of a physical medical device. It does not involve a "test set" in the context of evaluating an AI/ML algorithm's performance on a dataset. The "data" provenance mentioned is a "clinical literature review."

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. There was no "ground truth" to establish for a test set in the context of AI/ML performance. The review of clinical literature would have been performed by the manufacturer's regulatory and clinical affairs team to support their claims of safety and equivalence.

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

Not applicable. There was no "test set" requiring adjudication for AI/ML performance.

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-assisted device.

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

Not applicable. This is not an AI algorithm.

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

Not applicable in the context of AI/ML. The "ground truth" for this medical device submission is the established safety and effectiveness of the predicate devices and the scientific and engineering principles demonstrating the equivalence of the new device. The submission mentions a "clinical literature review" which would draw from published studies, which in turn might report on pathology, outcomes data, or expert clinical observations for the predicate and similar devices.

8. The sample size for the training set

Not applicable. There is no training set as this is not an AI/ML device.

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

Not applicable. There is no training set for which a ground truth needs to be established.

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