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The True™ Spinal Fixation System is intended for posterior, noncervical (T1-S1) pedicle fixation to provide immobilization and stabilization in skeletally mature patients as an adjunct to fusion for the treatment of the following acute and chronic instabilities or deformities of the thoracic, lumbar, and sacral spine: severe spondylolisthesis (grades 3 and 4) of the L5-S1 vertebra; degenerative spondylolisthesis with objective evidence of neurologic impairment, fracture, dislocation, spinal stenosis, scoliosis, kyphosis, lordosis, spinal tumor, pseudarthrosis and failed previous fusion.

The True™ Spinal Fixation System consists of pairs of longitudinal members (rods), anchors (polyaxial pedicle screws) and interconnections (rod-rod connectors). The changes include a rod to rod connector for ease of delivery, thermoformed instruments to replace the stainless steel instruments, a lower final torque value of 7.5Nm previously 8.5 Nm

The provided document describes a 510(k) submission for the True™ Spinal Fixation System, which is a medical device, not an AI/ML algorithm. Therefore, many of the typical acceptance criteria and study details relevant to AI/ML performance (like sample size for test sets, expert consensus for ground truth, MRMC studies, standalone performance, training sets, etc.) are not applicable here.

However, I can extract the relevant "acceptance criteria" and "study" details for this medical device as presented in the document, which primarily revolve around mechanical performance and substantial equivalence to predicate devices.

Here's the information based on the provided text, structured to best fit your request:

Acceptance Criteria and Device Performance for True™ Spinal Fixation System

Given that this is a 510(k) submission for a traditional medical device (spinal fixation system) and not an AI/ML algorithm, the "acceptance criteria" relate to mechanical performance and "substantial equivalence" to legally marketed predicate devices. The "study" refers to mechanical testing.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The primary "acceptance criterion" for 510(k) submissions is demonstrating "substantial equivalence" to one or more legally marketed predicate devices in terms of intended use, technological characteristics, and safety and effectiveness. The performance tests are conducted to support this claim, especially for any technological differences.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not explicitly stated in the document. Mechanical tests typically involve multiple samples to ensure statistical validity, but the exact number for "worst case construct" is not provided.
• Data Provenance: The tests were "performed according to ASTM F1717 and ASTM 1798." This implies the tests were conducted in a controlled environment, likely a lab, following standardized testing protocols. There is no information regarding country of origin or whether it was retrospective or prospective, as these terms typically apply to clinical data, not benchtop mechanical testing.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

• Not Applicable. For mechanical testing of a spinal fixation system, ground truth is established by objective physical measurements using calibrated equipment according to design specifications and industry standards (ASTM), not by expert human graders or medical professionals.

4. Adjudication Method for the Test Set

• Not Applicable. Adjudication methods like "2+1" typically apply to human expert review of clinical cases. For mechanical testing, quantitative measurements are taken, and compliance is determined against predefined physical specifications and standards.

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 a medical device for spinal fixation, not an AI/ML algorithm requiring human reader performance evaluation.

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

• Not Applicable. This is a physical medical device, not a software algorithm.

7. The Type of Ground Truth Used

• Benchtop Mechanical Measurement against ASTM Standards and Predicate Device Performance. The "ground truth" for the device's performance is established by its ability to meet the defined mechanical properties and safety profiles as set by FDA guidelines, industry standards (ASTM F1717, ASTM 1798), and by demonstrating "substantial equivalence" to the mechanical performance of legally marketed predicate devices.

8. The Sample Size for the Training Set

• Not Applicable. There is no "training set" as this is not an AI/ML algorithm.

9. How the Ground Truth for the Training Set was Established

• Not Applicable. There is no "training set" or corresponding ground truth for this type of device.

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