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510(k) Data Aggregation
(52 days)
SALVATION 3DI PLATING SYSTEM
The SALVATION™ 3Di Plaing System is indicated for the treatment of fracture stabilization/fixation, revision procedures, osteotonies, and reconstruction/arthrodesis of small bones, as well as patients with osteopenic bone. Specific examples include: medial column fusion (talus, navicular, cuboid, first metatarsal) for neuropathy (Charcot).
The SALVATION™ 3Di Plating System consists of titanium alloy plates and screws used for midfoot reconstruction. The system features plates of various sizes and styles, as well as locking and non-locking screws.
The provided text is a 510(k) summary for the SALVATION™ 3Di Plating System. This document focuses on demonstrating substantial equivalence to predicate devices rather than proving a device meets specific performance acceptance criteria through a full clinical study with a detailed test set, ground truth, and statistical analysis as would be done for a novel AI/software device.
Therefore, many of the requested categories are not applicable (N/A) in the context of this 510(k) submission.
Here's a breakdown based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
Material composition equivalent to predicates | "The SALVATION™ 3Di Plating System is technologically substantially equivalent to predicate devices in material..." (Page 2) |
Size equivalent to predicates | "The SALVATION™ 3Di Plating System is technologically substantially equivalent to predicate devices in material, size..." (Page 2) |
Bending strength equivalent to predicates | "...and bending strength." (Page 2) |
Acceptable static bending performance | Demonstrated through "Performance testing and analysis that demonstrated substantial equivalence includes static bending..." (Page 2) |
Acceptable insertion performance | Demonstrated through "...insertion..." (Page 2) |
Acceptable removal performance | Demonstrated through "...removal..." (Page 2) |
Acceptable ultimate torque performance | Demonstrated through "...and ultimate torque..." (Page 2) |
Acceptable pull-out rationale | Demonstrated through "...as well as a pull-out rationale." (Page 2) |
2. Sample size used for the test set and the data provenance
- N/A. The submission relies on non-clinical performance testing (in vitro/bench testing) rather than a clinical study with a 'test set' of human data. The specific sample sizes for these bench tests are not disclosed in this summary.
- Data Provenance: Not specified, but generally, such testing is performed in a controlled laboratory environment.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- N/A. As it's a non-clinical submission, there was no clinical test set requiring expert-established ground truth. Expert input would relate to the design and interpretation of the bench testing, but not in the same way as establishing ground truth for a diagnostic AI.
4. Adjudication method for the test set
- N/A. No clinical test set.
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
- N/A. This is a device for bone fixation, not an AI/software device intended to assist human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
- N/A. This is a physical medical device (bone plate system), not an algorithm or AI.
7. The type of ground truth used
- N/A. For this non-clinical submission, "ground truth" would correspond to the engineering specifications and results of the physical performance tests (e.g., measured bending strength, torque values). These are measured directly from the device components, not established via expert consensus, pathology, or outcomes data in a clinical sense.
8. The sample size for the training set
- N/A. This is not an AI/machine learning device that requires a training set. The "training" in this context would be the design iterations and engineering development of the physical device prior to the final performance testing.
9. How the ground truth for the training set was established
- N/A. As above, not an AI/ML device. The "ground truth" for the device's design and manufacturing would be based on established engineering principles, material science, and predicate device characteristics.
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