(105 days)
The ENSPLINTCMx™ Clavicle Pin is intended to be used to repair an acute fracture, mal-union or non-union of the clavicle.
The EnsplintCMxTM configuration consists of an implant made of 316 stainless steel.
This device is a traditional medical implant, not an AI/ML powered device. As such, the concept of "acceptance criteria" and "study that proves the device meets the acceptance criteria" in the context of AI/ML performance metrics (like sensitivity, specificity, or AUC) does not apply directly. Instead, the device's acceptance is based on demonstrating substantial equivalence to a predicate device through non-clinical performance data.
Here's a breakdown of the requested information based on the provided text, adapted for a non-AI device:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria (Demonstrated Substantial Equivalence to Predicate Device) | Reported Device Performance |
|---|---|
| Intended Use: Repair of acute fracture, mal-union, or non-union of the clavicle. | The EnsplintCMx™ is intended to be used to repair an acute fracture, mal-union, or non-union of the clavicle. (Matches predicate's intended use) |
| Performance Characteristics: Comparable mechanical and functional properties to the predicate. | Non-clinical (bench top and cadaver) laboratory testing demonstrates that the device is substantially equivalent in performance characteristics. |
| Materials: Use of similar materials as the predicate device. | The EnsplintCMx™ configuration consists of an implant made of 316 stainless steel. (Similar material to predicate, though specific predicate material not explicitly stated, it's implied by "similar materials"). |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size for Test Set: The document mentions "bench top and cadaver" testing. The specific number of samples (e.g., number of cadavers, number of devices tested in benchtop) is not provided.
- Data Provenance: The origin of the data (country) is not specified. The testing described is non-clinical, suggesting a laboratory setting, which aligns with prospective testing for device characterization.
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)
This question is not applicable as the device is a physical medical implant, not an AI diagnostic tool. There is no "ground truth" in the sense of expert annotation of medical images or data. The "ground truth" for this device would be established engineering and biomechanical principles and measurements.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This question is not applicable for a non-AI medical device. Adjudication methods are typically used to resolve discrepancies in expert labeling for AI training/testing. Performance was assessed through non-clinical (benchtop and cadaver) laboratory testing.
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
This question is not applicable for a non-AI medical device. MRMC studies are designed for assessing the impact of AI on human reader performance, which doesn't apply here. The device itself is an implant for physical fixation.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This question is not applicable as there is no algorithm involved. The performance evaluation was of the physical device's mechanical properties and function.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
As explained previously, the concept of "ground truth" in the context of expert consensus or pathology is not directly applicable to this type of device. The "truth" in this context is based on:
- Biomechanical testing results: Measuring parameters like strength, stiffness, fatigue resistance in laboratory (benchtop) settings.
- Anatomical compatibility: Assessing fit and function in cadaveric models.
- Material properties: Verifying that the 316 stainless steel meets established standards.
8. The sample size for the training set
This question is not applicable. As a non-AI device, there is no "training set" in the machine learning sense. Device design and validation are based on engineering principles, materials science, and non-clinical testing.
9. How the ground truth for the training set was established
This question is not applicable for the same reasons as point 8.
§ 888.3020 Intramedullary fixation rod.
(a)
Identification. An intramedullary fixation rod is a device intended to be implanted that consists of a rod made of alloys such as cobalt-chromium-molybdenum and stainless steel. It is inserted into the medullary (bone marrow) canal of long bones for the fixation of fractures.(b)
Classification. Class II.