Stabilization Clamp for Insertion of Deep Brain Stimulation Electrodes: Technical Note
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) electrodes are being implanted with increasing frequency for the management of movement disorders and chronic pain. Success with this neuro-augmentative technique requires accurate electrode lead placement. In order to enhance accuracy of final lead placement and ease of insertion, we describe a useful and reliable DBS electrode lead stabilization device developed and used at our centre. MATERIALS AND METHODS: The DBS electrode stabilization device consists of a 2-clamp system designed to fit the Leksell stereotactic frame. The clamps work in series to secure the stereotactic lead at the time of its final positioning in the desired subcortical target without the need of fluoroscopic control. RESULTS: The DBS electrode stabilization device has been used in 30 patients for 54 electrode implantations at our institution since 2000. Postoperative magnetic resonance imaging was performed in all cases and confirmed accurate placement of the electrodes. CONCLUSIONS: Accurate electrode lead placement is critical for the clinical efficacy of DBS systems. The simple and reliable stabilization device described here is easy to operate and enhances the final placement accuracy of DBS electrode leads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".