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The KLS-Martin Resorb-X System is intended for fractures of the craniofacial skeleton including, but not limited to, comminuted fractures of the naso-ethomoidal and infraorbital areas, comminuted fractures of the frontal sinus wall, and midface fractures; and reconstructive procedures of the midface or craniofacial skeleton.

The KLS-Martin Resorb-X System is NOT intended for use in the mandible or full load-bearing situations, nor in areas of active infection or for patients with conditions including blood supply limitations, insufficient quantity or quality of bone, or latent infections.

The KLS-Martin Resorb-X resorbable plating system consists of all necessary Instrumentation to employ three diameters of screws with associated plates and meshes. The screws are Poly (D. L)-Lactid-Acid (PDLLA) and are available in diameters 1.6mm, 2.1mm and 2.4mm. The plates and meshes are the same material and vary in shape, size and thickness, and may be used with any of the above diameter screws to fixate.

The screws and plates are provided pre-sterilized by gamma irradiation and are not intended to be resterilized by the end user.

The screws are not intended to be used without a plate.

The screws require a pilot hole and this pilot hole must be tapped.

The plates are not intended to be used for fracture compression.

The Resorb-X system is not intended for use in the mandible.

The provided text is a 510(k) summary for the Resorb-X Resorbable Plating System, which is a medical device application for market clearance. It primarily focuses on demonstrating substantial equivalence to predicate devices. This type of document, particularly from 2002, does not typically contain the detailed information required to describe the acceptance criteria and a study that proves the device meets those criteria in the context of software or AI performance.

Here's why and what information can be extracted:

• Device Type: The Resorb-X system is a physical implant (plating system) made of Poly (D.L)-Lactid-Acid (PDLLA). Its performance criteria would be related to mechanical strength, biocompatibility, degradation rate, and clinical outcomes, not algorithmic performance.
• 510(k) Process: The 510(k) process for such devices relies heavily on demonstrating substantial equivalence to existing legally marketed predicate devices, often through bench testing and sometimes animal studies, but rarely through large-scale human clinical trials with detailed statistical performance metrics like sensitivity/specificity for diagnostic aids.
• Date: The document is from 2002, a time when AI in medical devices, especially as a standalone or assistance tool for diagnosis, was not prevalent in regulatory submissions in the same way we see today.

Therefore, most of the requested fields related to acceptance criteria for AI performance and AI study details cannot be filled from this document.

However, I can extract the general indications for use and contraindications, which act as a form of "acceptance criteria" for the device's intended application.

Here's a breakdown of what can be inferred or explicitly stated from the provided text, and where gaps exist relative to your request:

1. Table of Acceptance Criteria and Reported Device Performance

• Note: The FDA's substantial equivalence determination itself is the "acceptance criterion" for market clearance in a 510(k). The specific performance metrics (e.g., tensile strength, degradation rate, biocompatibility) for the device would have been compared to the predicate devices during the submission process, but these detailed engineering/biological performance criteria and their values are not present in this summary.

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

• Not applicable for AI/software performance. This device is a physical implant. Performance would typically be evaluated through bench testing (e.g., mechanical stress tests), biocompatibility testing (in vitro/in vivo animal models), and sometimes limited human clinical data or post-market surveillance. Such details are not included in this 510(k) summary, which focuses on substantial equivalence.

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

• Not applicable for AI/software performance. Ground truth, in the context of AI, refers to expert-labeled data. For a physical implant, "ground truth" might relate to histological analysis, imaging results of healing, or clinical outcomes, typically assessed by surgeons or pathologists, but not in the format requested for AI validation.

4. Adjudication Method for the Test Set

• Not applicable for AI/software performance. Adjudication methods like 2+1 or 3+1 are for reconciling disagreements among human readers/experts in AI model validation. This concept doesn't apply to the regulatory submission for a physical implant.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, not performed. MRMC studies are specifically designed to evaluate the impact of a diagnostic or interpretive tool (often AI-assisted) on human reader performance. This device is a physical fixation system, not a diagnostic or interpretive tool.

6. Standalone (Algorithm Only) Performance Study

• No, not applicable. This is a physical device, not an algorithm.

7. Type of Ground Truth Used

• Not applicable in the AI sense. For a physical implant, "ground truth" would be established through:
  • Bench Testing Data: Mechanical properties (e.g., strength, stiffness, fatigue life).
  • Biocompatibility Testing: Histological analysis, inflammatory response markers (often in animal models).
  • Degradation Studies: Material science analysis of how the polymer degrades over time in physiological conditions.
  • Clinical Outcomes (if any were part of the predicate device's history or limited trials): Patient healing, absence of complications, device integrity from follow-up imaging (e.g., X-rays).
  • The 510(k) relies on the "ground truth" established for the predicate devices through their own regulatory pathways and clinical experience.

8. Sample Size for the Training Set

• Not applicable. This device does not involve a "training set" in the machine learning sense. Material properties, design principles, and manufacturing processes are developed through engineering and material science, not by training an algorithm on a dataset.

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

• Not applicable. This device does not involve a "training set" for an algorithm.

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