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The Implex Hedrocel® Replacement Cup Insert, cemented, is intended for use as a in-situ replacement polyethylene bearing surface under circumstances of joint instability, wear and/or damage caused by the patient during use.

The Implex Hedrocel® Replacement Cup Inserts, cemented, are compatible with the family of Hedrocel® acetabular cups in OD sizes from 40 to 70 mm. The replacement inserts are available with four ID size options (22 mm, 26 mm, 28 mm and 32 mm) and in 0°, 10°, and 20° face angles.

This document describes the regulatory submission for the Implex Hedrocel® Replacement Cup Insert, a medical device. The information provided heavily focuses on regulatory compliance and substantial equivalence to predicate devices, rather than detailed performance study results in the context of typical AI/software acceptance criteria.

Therefore, many of the requested details regarding acceptance criteria, study design for AI, expert involvement, and ground truth establishment are not directly applicable or available in the provided text. The device is a physical medical implant, not an AI/software product.

However, I can extract the relevant information within the context of a traditional medical device submission if analogy to AI acceptance criteria is made.

Acceptance Criteria and Device Performance (Analogous to AI/Software)

For this physical medical device, the primary "acceptance criterion" from a regulatory perspective is substantial equivalence to predicate devices. This is established by demonstrating that the device has similar technological characteristics and performs comparably in relevant tests.

Breakdown of Study Information (Interpreted for a Physical Device)

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

   • Sample Size: Not explicitly stated. For a physical device like this, the "test set" would refer to the number of devices or components subjected to mechanical testing. This information is typically found in the full test reports, not the summary K983128 document.
   • Data Provenance: The document states "Testing conducted to evaluate the fatigue characteristics of the device under defined laboratory conditions." This indicates prospective laboratory testing. The country of origin of the data is not specified, but given the submitter's address (Allendale, New Jersey, USA) and the FDA submission, it's highly probable the testing was conducted in the USA or by a facility adhering to US regulatory standards.

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

   • Not Applicable in the AI Sense: For a physical device, "ground truth" for mechanical testing is established by engineering standards and measurement accuracy, not by human experts interpreting data. The "experts" would be the engineers and technicians designing and conducting the tests, ensuring they follow recognized standards (e.g., ISO, ASTM for material properties, fatigue, wear). Their qualifications would be in materials science, biomechanical engineering, or related fields. The document does not specify the number or qualifications of these testing personnel.

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

   • Not Applicable: Adjudication is typically for resolving discrepancies in human interpretation (common in AI studies or clinical trials). For mechanical testing, if there were any ambiguities in results, standard statistical methods or re-testing would be employed, not an adjudication panel.

4. 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 physical medical device. MRMC studies, human reader improvement, and AI assistance are concepts specific to diagnostic AI or image analysis software.

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

   • Not Applicable: This is a physical medical device. There is no algorithm or human-in-the-loop performance component. The "standalone" performance here refers solely to the mechanical performance of the implant itself in a laboratory setting.

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

   • The "ground truth" for this device's performance would be the physical properties and mechanical integrity measurements derived from standardized laboratory tests (e.g., resistance to fatigue under specified load cycles). These are objective measurements following established engineering protocols, rather than subjective human interpretation, pathology, or patient outcomes data as "ground truth" for a performance claim in this type of submission. Patient outcomes are relevant for post-market surveillance.

7. The sample size for the training set:

   • Not Applicable: This is a physical medical device. There is no "training set" in the context of machine learning. The design and manufacturing processes are refined through engineering development based on material science principles and prior device knowledge, not an iterative training process on data.

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

   • Not Applicable: As there is no training set in the AI sense, there's no ground truth established for it. The analogous process for a physical device would be the verification and validation of manufacturing parameters and material specifications against industry standards and design requirements.

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