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510(k) Data Aggregation
(71 days)
EMERGE MEDICAL SMALL AND LARGE NON-LOCKING FRAGMENT PLATE SYSTEM
The Emerge Medical Large Plate System is intended for fractures, osteotomies, nonunions, deformations, revisions, replantations, of bones and bone fragments including clavicle, olecranon, ulna, humerus, femur, tibia, fibula, calcaneus, tarsals, and pelvis, including in osetopenic bone.
The Emerge Medical Small Plate System is intended for fractures, osteotomies, nonunions, deformations, revisions, replantations, of bones and bone fragments including clavicle, scapula, olecranon, humerus, radius, ulna, tibia, fibula, tarsals, metatarsals, phalanges, and calcaneus, including in osetopenic bone.
The Emerge Medical Small and Large Non-Locking Fragment Plate System consists of stainless steel components. The plates are available in a variety of lengths with the number of holes varying depending on plate length. The plates are provided non-sterile.
Here's a breakdown of the acceptance criteria and the study details for the Emerge Medical Small and Large Non-Locking Fragment Plate System, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
Acceptance Criteria (Stated Goal) | Reported Device Performance (Test Results) |
---|---|
Strength sufficient for intended use. | The results of non-clinical testing show that the strength of the Emerge Medical Small and Large Non-Locking Fragment Plate System is sufficient for its intended use. |
Substantially equivalent to legally marketed predicate devices in terms of strength. | The device is substantially equivalent to legally marketed predicate devices. |
2. Sample Size Used for the Test Set and Data Provenance:
The provided text describes non-clinical mechanical testing and does not refer to human subject data or a "test set" in the context of clinical trials or AI/algorithm performance. Therefore, details about sample size (patients/cases) or data provenance (country, retrospective/prospective) are not applicable here. The testing was performed on the device itself.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
Ground truth, in this context, would relate to the established standards or methods for evaluating the mechanical properties of bone plates. The text does not mention human experts involved in establishing a "ground truth" for the mechanical tests. The testing was performed according to a modification of a recognized standard (ASTM F382-99 (2008)).
4. Adjudication Method for the Test Set:
Not applicable, as this was mechanical testing, not a clinical study involving human assessment or adjudication.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
No, an MRMC comparative effectiveness study was not conducted. This is a medical device (bone plate) and the performance data consists of mechanical strength testing, not an assessment of human readers with or without AI assistance.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:
Not applicable. This device is a physical bone plate, not an algorithm or AI system. Therefore, standalone performance in the context of AI is not relevant.
7. Type of Ground Truth Used:
The ground truth used for evaluating the device's performance was the modified ASTM F382-99 (2008) standard for dynamic four-point bending. This standard provides a recognized methodology for assessing the mechanical strength of bone fixation devices.
8. Sample Size for the Training Set:
Not applicable. This device is a physical bone plate, not an algorithm that requires a training set.
9. How the Ground Truth for the Training Set Was Established:
Not applicable, as there is no training set for this type of medical device.
In summary, the provided document focuses on the mechanical performance of a bone plating system, demonstrating its equivalence to existing predicate devices through non-clinical laboratory testing. The evaluation does not involve clinical studies with human participants, AI algorithms, or the associated concepts of test sets, training sets, or expert ground truth as typically applied in AI/clinical performance assessments.
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