Placement of deep brain stimulator electrodes using real‐time high‐field interventional magnetic resonance imaging
Bibliographic record
Abstract
A methodology is presented for placing deep brain stimulator electrodes under direct MR image guidance. The technique utilized a small, skull-mounted trajectory guide that is optimized for accurate alignment under MR fluoroscopy. Iterative confirmation scans are used to monitor device alignment and brain penetration. The methodology was initially tested in a human skull phantom and proved capable of achieving submillimeter accuracy over a set of 16 separate targets that were accessed. The maximum error that was obtained in this preliminary test was 2 mm, motivating use of the technique in a clinical study. Subsequently, a total of eight deep brain stimulation electrodes were placed in five patients. Satisfactory placement was achieved on the first pass in seven of eight electrodes, while two passes were required with one electrode. Mean error from the intended target on the first pass was 1.0 +/- 0.8 mm (range = 0.1-1.9 mm). All procedures were considered technical successes and there were no intraoperative complications; however, one patient did develop a postoperative infection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".