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510(k) Data Aggregation
(247 days)
The General System is intended for posterior pedicle screw fixation of the non-cervical posterior spine in skeletally mature patients. It provides stabilization of spinal segments as an adjunct to fusion in the treatment of the following acute and chronic instabilities or deformities: (1) trauma (i.e. fracture or dislocation), (2) curvatures (scoliosis, kyphosis, and/or lordoss), (3) spinal tumor, (4) failed previous fusion, (6) spinal stenosus. It is not intended for pedicle screw fixation above T8. This device may be used with autograft and/or allograft.
The General Spinal System consists of Fixed-Angle Reduction Screws. Rods, Cross Link and set screws. It is made of Titanium Alloy (Ti6Al4VELI), which meets ASTM F136-02a, Standard Specification for Wrought Titanium-6 Aluminum-4 Vanadium ELI (Extra Low Interstitial) Alloy for Surgical Implant Applications, which are widely used for surgical implants with well known biocompatibility. The proposed devices are provided non-sterile. It is required to be sterilized via autoclave method to reach a SAL of 10 by the hospital prior to surgery. The recommended sterilization method was validated per ISO 17665-1: 2006 Sterilization of health care products -- Moist heat -- Part 1: Requirements for the development, validation, and routine control of a sterilization process for medical devices
The provided document is a 510(k) summary for the "General Spinal System," a pedicle screw spinal system. It describes the device, its intended use, and states that non-clinical tests were conducted to demonstrate substantial equivalence to predicate devices. However, the document does not contain specific acceptance criteria, reported device performance metrics, detailed study designs (such as sample sizes, ground truth establishment, or expert qualifications), or the results of comparative effectiveness studies (MRMC) or standalone algorithm performance studies that would be necessary to answer the questions thoroughly.
Therefore, I can only provide information based on what is available in the document.
Detailed Breakdown based on the provided document:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state quantitative acceptance criteria in terms of specific performance metrics (e.g., specific load endurance values, displacement limits) that a device must meet, nor does it report specific numerical performance results for the device against such criteria.
Instead, the document states: "Non clinical tests were conducted to verify that the proposed device met all design specifications as was Substantially Equivalent (SE) to the predicate device. The test results demonstrated that the proposed device complies with the following standard: ASTM F1717-04, Standard Test Methods for Spinal Implant Constructs in a Vertebrectomy Model, including the following items: Static compression bending test; Dynamic compression bending test; Static torsion test."
This implies that the acceptance criteria are adherence to the ASTM F1717-04 standard's methodologies and achieving comparable performance to the predicate device(s) within the scope of these tests. Without specific data, a table cannot be constructed with numerical values.
| Acceptance Criteria (Implied) | Reported Device Performance (Summary) |
|---|---|
| Compliance with ASTM F1717-04 for Spinal Implant Constructs in a Vertebrectomy Model | The device's non-clinical tests demonstrated compliance with ASTM F1717-04 for static compression bending, dynamic compression bending, and static torsion tests. The device was deemed Substantially Equivalent (SE) to predicate devices based on these tests. |
2. Sample size used for the test set and the data provenance
The document describes non-clinical mechanical tests. It does not involve human data or test sets in the context of clinical trials. Therefore, "sample size used for the test set" and "data provenance (e.g. country of origin of the data, retrospective or prospective)" are not applicable in the context of this device's testing as described. The testing was likely conducted in a laboratory setting.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This question is not applicable as the document describes non-clinical mechanical testing, not a study involving ground truth established by experts (e.g., radiologists, pathologists). The "ground truth" for mechanical testing is typically defined by the test standard (e.g., ASTM F1717-04) and the physical properties observed.
4. Adjudication method for the test set
This question is not applicable for the same reason as points 2 and 3. Adjudication methods like 2+1 or 3+1 are used in clinical studies or expert consensus scenarios, not for mechanical bench 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. The device is a "General Spinal System" (a pedicle screw spinal system), which is a physical implant, not an AI or imaging diagnostic device. Therefore, MRMC studies and AI assistance are irrelevant to this device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This question is not applicable. The device is a physical medical implant, not an algorithm or software.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
For the non-clinical tests described, the "ground truth" refers to the established performance characteristics and requirements outlined in the ASTM F1717-04 standard for spinal implant constructs, as well as the observed mechanical properties (e.g., strength, stiffness, fatigue life) of the tested device and its predicate(s). It is purely objective mechanical measurement, not a subjective "truth" established by experts or clinical outcomes.
8. The sample size for the training set
This question is not applicable. The document describes the testing of a physical medical device, not an AI/ML algorithm that requires a "training set."
9. How the ground truth for the training set was established
This question is not applicable for the same reason as point 8.
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