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The Trident® Constrained Acetabular Insert is intended for use as a component of a total hip prosthesis in primary or revision patients at a high risk of hip dislocation due to a history of dislocation, bone loss, joint or soft tissue laxity, neuromuscular disease or intraoperative instability.

The Trident® Constrained Acetabular Insert is a specific insert designed to accept a Stryker UHR® bipolar head. The bipolar head is preassembled to the insert and securely retained by a titanium alloy retaining ring. This submission details two separate modifications to the predicate Trident Constrained Acetabular Insert. The first modification is described as the subject Trident® 0° Constrained Acetabular Insert. The subject Trident® 0° Constrained Acetabular Insert has a neutral (0°) face instead of the 10° face on the predicate Trident® Constrained Acetabular Insert. The second modification is described as the subject Trident® All-Poly Constrained Acetabular Insert. The subject Trident® All-Poly Constrained Acetabular Insert has a neutral (0°) face instead of the 10° face on the predicate Trident® Constrained Acetabular Insert and a modified external geometry to allow for cement fixation.

This is a 510(k) summary for a medical device, specifically a line extension to an existing acetabular insert. It does not contain information about studies involving expert consensus, human readers, or AI. The assessment for this device is based on "substantial equivalence" to predicate devices, which is demonstrated through mechanical testing and analysis rather than clinical trials with human subjects.

Therefore, many of the requested categories are not applicable to the provided document.

Here's a breakdown of the available information:

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

The document does not specify quantitative acceptance criteria in a tabled format for performance. Instead, it states that "Mechanical testing and analysis demonstrates substantial equivalence of the subject components to the predicate devices in regards to mechanical strength." This implies that the performance (mechanical strength) of the new components met the implicit criteria for substantial equivalence to the predicate devices.

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

The document does not specify the sample size for mechanical testing. It mentions "mechanical testing and analysis," which typically involves a set number of physical samples, but the exact number is not provided. The data provenance is not specified beyond being part of the submission to the FDA. It's almost certainly laboratory-based testing, not clinical data, and therefore not retrospective or prospective in the usual clinical sense.

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. The assessment is based on mechanical testing of the device itself, not on interpretation of data by clinical experts.

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

Not applicable. There was no clinical ground truth requiring adjudication.

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 a study involving human readers or AI.

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

Not applicable. This is not a study of an algorithm.

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

The "ground truth" for this submission is based on mechanical strength as determined by engineering tests, and a comparison to the mechanical strength of legally marketed predicate devices. There is no clinical or pathological "ground truth" involved in this type of submission.

8. The sample size for the training set

Not applicable. This is not a machine learning or AI device that would require a training set.

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

Not applicable.

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