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The DPR Cable is intended for:

• Spinal applications include sublaminar and intraspinous process wiring for trauma applications.
• Trochanteric reattachment after trochanteric osteotomy following total hip arthroplasty.
• Sternotomy indications including the "rewiring" of osteomized sternums.
• Trauma surgery indications including olecranon, ankle, patella and some shoulder fracture rewiring.
  The device is intended for single patient use only.

The DPR Cable is a flexible, multi-strand ultra-high molecular weight polyethylene (UHMWPE) cerclage cable. The device is intended to provide stabilization of bony segments.
The DPR Cable is made of woven ultra-high molecular weight polyethylene fibers which incorporate bismuth trioxide (Bi₂O₃). The addition of bismuth trioxide allows for radiographic visualization both during and after surgical procedures. The device is supplied sterile in double-layer packages. The DPR Cable is intended for single patient use only. DPR Cable is MR safe.

The provided text is a 510(k) Premarket Notification for a medical device called the "DPR Cable." This document primarily focuses on establishing substantial equivalence to a predicate device for regulatory approval, rather than detailing a clinical study with acceptance criteria for an AI/algorithm-based device.

Therefore, many of the requested details regarding acceptance criteria, study design for proving device performance (especially for an AI/algorithm), ground truth establishment, sample sizes for training/test sets, expert adjudication, and MRMC studies do not apply to this type of regulatory submission for this medical device.

The document discusses biomechanical testing to show equivalence in performance characteristics to predicate devices, but this is not an AI/algorithm study.

Here's an attempt to extract relevant information, highlighting where the requested details are not applicable based on the provided text's context as a 510(k) for a physical medical device:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria here are based on demonstrating equivalence to predicate devices through biomechanical testing, rather than explicit criteria for an AI's accuracy or clinical performance. The table in the document (Page 5) compares the technological characteristics and certain performance metrics with the predicate device.

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

• Sample Size: The document does not specify exact sample sizes for biomechanical tests like tensile strength or fatigue strength, only the results of these tests. This is not a clinical "test set" in the context of an AI/algorithm.
• Data Provenance: Not applicable. The data comes from in-vitro biomechanical testing of the device itself, not from clinical patient data.

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

• Not applicable. Ground truth, in the context of AI studies, refers to expert labeling of data. For this physical device, "ground truth" would be established by validated engineering tests and measurements, comparing against known standards or predicate device performance. No human experts are detailed for "ground truth" establishment in this context.

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

• Not applicable. This concept pertains to resolving discrepancies in expert labeling of data, which is not relevant for biomechanical testing of a physical device.

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 algorithm.

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

• For biomechanical testing, the "ground truth" is provided by established engineering standards and measurements for properties like tensile strength, fatigue strength, creep, and knot strength. It's an objective measurement of physical properties.

8. The sample size for the training set:

• Not applicable. This is not an AI/machine learning study.

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

• Not applicable.

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