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The Libra Pedicle Screw System is intended for immobilization of the posterior thoracic, lumbar and sacral/iliac spine (TI-S1/Ilium) in skeletally mature patients as an adjunct to the following indications: degenerative disc disease (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies), spondylolisthesis, trauma (i.e., fracture or dislocation), spinal stenosis, curvatures (i.e., scoliosis, kyphosis, or lordosis), tumor, pseudarthrosis, and/or failed previous fusion.

The Libra Pedicle Screw System consists of longitudinal members (rods), anchors (screws) and fasteners in a variety of sizes to accommodate differing anatomic requirements.

This document describes the FDA's clearance of the Libra Pedicle Screw System (K151770) based on substantial equivalence to predicate devices, rather than a study and acceptance criteria for a novel device's performance. Therefore, the requested information about device performance, sample sizes, ground truth establishment, expert adjudication, and MRMC studies is not directly available in this text.

However, I can extract information related to the performance data provided for substantial equivalence and the acceptance criteria implicitly met through comparison with predicates.

Implicit Acceptance Criteria and Reported Device Performance (Table)

Since this is a 510(k) summary for a substantial equivalence determination for a medical device (pedicle screw system), the "study" is primarily focused on bench testing to demonstrate that the new device performs as safely and effectively as legally marketed predicate devices, not clinical studies with human performance metrics as one might see for AI/software devices. Therefore, many of your specific questions are not directly applicable or answerable from this document.

Here's an attempt to address your other points based on the provided text, acknowledging the nature of a 510(k) submission for a physical implantable device:

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

• Test Set Sample Size: The document does not specify the exact number of Libra Pedicle Screw System constructs tested for mechanical performance. It generally refers to "worst case Libra Pedicle Screw System constructs." For a 510(k) for a physical device, this typically involves a representative number of samples (e.g., typically 3 to 5 per test condition) to satisfy the standard (ASTM F1717). The specific number is usually detailed in the full 510(k) submission, not summarized at this level.
• Data Provenance: The data is from bench testing (mechanical testing), conducted by the manufacturer, Spinal Balance Inc., to evaluate the physical components of the device. It is not clinical data, and thus concepts like "country of origin of the data" or "retrospective/prospective" do not apply in the same way they would for clinical or AI data.

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

• Not applicable in this context. For mechanical bench testing, "ground truth" is established by the specifications and pass/fail criteria of the referenced ASTM standards (ASTM F1717). Engineers and technicians conduct the tests and determine if the results meet the criteria, comparing them against the performance of similar predicate devices. There isn't "expert adjudication" of test results in the sense of clinical image review for diagnostic accuracy.

4. Adjudication method for the test set

• Not applicable. See point 3. Testing against an industry standard (ASTM F1717) provides objective pass/fail criteria.

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 device is a physical spinal implant, not an AI software. Therefore, MRMC studies and the concept of "human readers improving with AI assistance" are irrelevant to this submission.

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 an algorithm.

7. The type of ground truth used

• For mechanical testing, the "ground truth" is adherence to the performance standards and specifications outlined in ASTM F1717, and the comparative performance to predicate devices as demonstrated by their own cleared mechanical testing data. The goal is to show equivalent, safe, and effective mechanical properties.

8. The sample size for the training set

• Not applicable. This is a physical medical device, not a machine learning model, so there is no training set in the AI sense.

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

• Not applicable. See point 8.

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