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510(k) Data Aggregation
(32 days)
The Streamline OCT Occipito-Cervico-Thoracic System is intended to provide immobilization of spinal segments as an adjunct to fusion when using autograft for the following acute and chronic instabilities of the craniocervical junction, the cervical spine (C1 to C7) and the thoracic spine (T1 to T3): traumatic spinal fractures and/ or traumatic dislocations; instabilitity of deformity; failed previous fusions (e.g., pseudarthrosis); tumors involving the cervical/thoracic spine; and degenerative disease, including intractable radiculopathy and/or arm pain of discogenic origin as confirmed by radiographic studies, and degenerative disease of the facets with instability.
The system is also intended to restore the integrity of the spinal column even in the absence of fusion for a limited time period in patients with advanced stage tumors involving the cervical spine in whom life expectancy is of insufficient duration to permit achievement of fusion.
In order to achieve additional levels of fixation, the Streamline OCT System may be connected to the Quantum Spinal Fixation System, Streamline MIS Spinal Fixation System or Streamline TL Spinal System using connectors and/or transition rods.
The Streamline OCT System consists of a variety of rods, hooks, polyaxial screws, high-angle screws, locking caps, occipital plates, occipital screws, and connecting components used to build an occipito-cervico-thoracic spinal construct. System components are manufactured from ASTM F136 medical grade titanium alloy and ASTM F1537 medical grade cobalt chromium molybdenum alloy. Medical grade titanium alloy and medical grade cobalt chromium molybdenum alloy may be used together. The system should be implanted using only the surgical instruments designed for the system. Cases and caddies are supplied for sterilization and transport of the implants and instruments. The purpose of this submission is to modify and add components to the system.
This document is a 510(k) premarket notification for a medical device called the "Streamline OCT Occipito-Cervico-Thoracic System." This kind of document focuses on demonstrating that a new device is "substantially equivalent" to existing legally marketed devices, rather than establishing acceptance criteria and proving performance against them in the same way one might for a novel device or software.
Therefore, the requested information, particularly regarding acceptance criteria, specific performance metrics, sample sizes for training/test sets, ground truth establishment by experts, and MRMC studies, is not typically found or required in a 510(k) submission for this type of device (spinal fixation system). The demonstration of substantial equivalence relies more on comparing the new device's design, materials, indications for use, and mechanical performance to existing predicate devices.
However, I can extract the information that is available in the document that somewhat aligns with your request:
1. A table of acceptance criteria and the reported device performance
There isn't a table of "acceptance criteria" and reported device performance in the sense of accuracy, sensitivity, or specificity for an AI/software device. Instead, the "acceptance criteria" are implied by the demonstration of substantial equivalence through non-clinical mechanical testing. The "reported device performance" refers to the results of these mechanical tests compared to predicate devices.
| Acceptance Criterion (Implied) | Reported Device Performance |
|---|---|
| Components do not introduce new worst-case scenarios. | Engineering analysis, including ASTM F1798 testing, confirmed that subject components do not introduce new worst-case components or cause the system to be more susceptible to loosening or failure. |
| Mechanical performance is substantially equivalent to predicates. | Mechanical construct testing (ASTM F1717 and ASTM F2706) demonstrated that the subject and predicate systems are substantially equivalent. No new risks to safety or effectiveness were raised by the non-clinical testing. |
| No new risks to safety or efficacy. | The submission demonstrates there are no new risks to safety or efficacy raised by the subject Streamline OCT System. |
2. Sample size 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: Not applicable in the context of this 510(k) for a spinal fixation system. There isn't a "test set" of clinical data or images as typically understood for AI/software. The testing involved mechanical tests of the devices themselves.
- Data Provenance: Not applicable. The "data" comes from mechanical testing conducted in a laboratory setting, not clinical patient data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. Ground truth as established by clinical experts for a test set is not relevant for this type of device's 510(k) submission. Mechanical testing relies on engineering standards and measurements.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. This concept is for clinical data review, not mechanical 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
Not applicable. This device is a physical spinal fixation system, not an AI or software device that assists human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
Not applicable. This is a physical medical device, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
For the mechanical testing, the "ground truth" is established by validated engineering test standards (ASTM F1798, ASTM F1717, and ASTM F2706) and the results derived from those specified testing protocols.
8. The sample size for the training set
Not applicable. There is no "training set" for a physical medical device. Device design and refinement are based on engineering principles, materials science, and testing, not machine learning training.
9. How the ground truth for the training set was established
Not applicable (as there's no training set). The design and development of the device components are informed by established engineering practices and biomechanical understanding of spinal anatomy and mechanics.
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