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510(k) Data Aggregation
(204 days)
STRYKER RF MULTI-LESION GENERATOR, MODEL 0406-900-000
The Stryker RF Multi-Lesion Generator, in combination with the Stryker RF Electrodes and Cannulae are intended for coagulation of soft tissues in orthopedic, spinal, and neurosurgical applications. Examples include, but are not limited to: Facette Denervation, Percutaneous Chordotomy/Dorsal Root Entry Zone (DREZ) Lesion, Trigeminus Neuralgia, Peripheral Neuralgia and Rhizotomy.
The Stryker RF Multi-Lesion Generator in combination with a Smith & Nephew SPINECATH™ & Acutherm™ catheters are intended for coagulation and decompression of disc material to treat symptomatic patients with annular disruption of contained herniated discs.
The Stryker RF Multi Lesion Generator will be used in conjunction with Stryker RF Electrodes and Cannula for ablation and coagulation of soft tissue. The generator applies temperature controlled radio frequency RF energy into targeted tissue via an electrode probe, resulting in cellular necrosis. In the case of interventional pain applications, pain relief is achieved by creating defined lesions on pain-conducting nerve fibers or tissue. In addition, the Stryker RF Multi-Lesion Generator allows multiple areas to be targeted and treated concurrently. By treating multiple areas concurrently, overall procedure time can be reduced while maintaining thermal treatments each equivalent to an individual treatment
The Stryker RF Multi-lesion System is a bipolar and monopolar, high frequency electrosurgical system. The System consists of the following components: a RF generator, footswitch hand controller, monopolar and bipolar electrodes, cannulae, neutral electrodes, coaxial bipolar electrodes and cannula, parallel bipolar adapter, connecting cables, and intradiscal cable,
Here's a summary of the acceptance criteria and study information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
The provided document does not explicitly state specific acceptance criteria or quantitative performance metrics for the Stryker RF Multi-lesion Generator. The basis for clearance is a demonstration of "substantial equivalence" to predicate devices.
Acceptance Criteria | Reported Device Performance |
---|---|
Not specified | Substantially equivalent to predicate devices (K032601 Stryker Interventional Pain RF Generator and K052878 Neurotherm NT 1000 RF Lesioning System) in terms of intended use, safety, and effectiveness. |
2. Sample Size Used for the Test Set and Data Provenance:
The provided document does not mention a test set, sample size for testing, or data provenance (e.g., country of origin, retrospective/prospective). The submission relies on a comparison to predicate devices rather than a new clinical study with a specific test cohort.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
Not applicable, as a specific test set requiring expert-established ground truth is not described in the document.
4. Adjudication Method for the Test Set:
Not applicable, as a specific test set requiring adjudication is not described in the document.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
The document does not mention an MRMC comparative effectiveness study. The clearance is based on substantial equivalence, not on demonstrating an improvement in human reader performance with or without AI assistance.
6. Standalone Performance Study (Algorithm Only Without Human-in-the-Loop Performance):
This is not applicable as the device is a radiofrequency lesion generator, a hardware device, not an algorithm or AI.
7. Type of Ground Truth Used:
Not applicable, as a specific test set requiring ground truth is not described in the document. The basis for clearance is "comparison to the predicate devices" for safety and effectiveness.
8. Sample Size for the Training Set:
This is not applicable as the device is a hardware product, not an AI/ML algorithm that requires a training set.
9. How the Ground Truth for the Training Set Was Established:
This is not applicable for the same reason as above – the device is not an AI/ML algorithm.
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