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The Quantum Spinal System components are non-cervical spinal fixation devices intended for use as a pedicle screw system (T1 - S2), a posterior hook and sacral/iliac screw fixation system or as an anterolateral fixation system (T8 - L5). Pedicle screw fixation is limited to skeletally mature patients. These devices are indicated for all of the following indications regardless of the intended use: degenerative disc disease (defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies), spondylolisthesis, trauma, (i.e., fracture or dislocation), deformities or curvatures (i.e., scoliosis, kyphosis, and/or lordosis, Scheuermann's Disease), tumor, stenosis, pseudoarthrosis, and failed previous fusion.

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This document describes the Quantum Spinal System, a medical device, and its regulatory clearance through the 510(k) pathway. It does not contain information about acceptance criteria or a study that proves the device meets specific performance criteria in the way that an AI/ML device submission would.

The provided text describes a traditional medical device (spinal fixation system) and its substantial equivalence to predicate devices, rather than an AI/ML device with performance metrics like accuracy, sensitivity, or specificity. Therefore, many of the requested fields are not applicable to the information contained in the provided document.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

This information is not provided in a quantifiable way typical for AI/ML device performance. The document states:

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

• Not applicable / Not provided. This document describes a spinal implant, not a data-driven device that requires a "test set" in the AI/ML sense. The performance data refers to mechanical and material testing, not clinical data sets.

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

• Not applicable / Not provided. Ground truth, in the context of AI/ML, refers to labels or diagnoses provided by experts on a dataset. This information is not relevant to the clearance of a spinal implant.

4. Adjudication method for the test set:

• Not applicable / Not provided. Adjudication methods are used in AI/ML studies to resolve disagreements among experts in labeling data. This is not pertinent to the current device.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

• Not applicable / Not provided. This device is a surgical implant, not an AI-powered diagnostic or assistive tool. Therefore, an MRMC study comparing human readers with and without AI assistance is irrelevant.

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

• Not applicable / Not provided. There is no algorithm for this device.

7. The type of ground truth used:

• Not applicable / Not provided. The "ground truth" for a spinal implant's safety and effectiveness is established through engineering testing (e.g., biomechanical strength, material compatibility), pre-clinical studies, and clinical experience with similar predicate devices, rather than a "ground truth" for data labeling. The regulatory decision is based on substantial equivalence to predicate devices which have an established safety and effectiveness profile.

8. The sample size for the training set:

• Not applicable / Not provided. This device does not involve machine learning; therefore, there is no "training set."

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

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

In summary: The provided document is a 510(k) summary for a traditional medical device (spinal fixation system). The regulatory review for such devices focuses on demonstrating substantial equivalence to legally marketed predicate devices, primarily through engineering performance data (e.g., mechanical testing conforming to ASTM standards), material safety, and similarity in design and intended use. The concepts of "acceptance criteria" and "study proving device meets acceptance criteria" for AI/ML devices, involving test sets, ground truth, experts, and reader studies, are not applicable to this type of traditional device clearance.

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