DUAL MICROELECTRODE TECHNIQUE FOR DEEP BRAIN STEREOTACTIC SURGERY IN HUMANS
Bibliographic record
Abstract
OBJECTIVE: To improve functional stereotactic microelectrode localization of small deep brain structures by developing and evaluating a recording system with two closely separated independently controlled microelectrodes. METHODS: Data were obtained from 52 patients using this dual microelectrode technique and 38 patients using the standard single microelectrode technique for subthalamic nucleus localization in patients with Parkinson's disease. RESULTS: There was a decrease in the incidence of noncontributory trajectories, defined as a single penetration made by the pair of closely spaced parallel microelectrodes, owing to microelectrode failure (from 7.2% to <1%), an improved localization and verification of nuclear borders, and a significant decrease in the number of trajectories used to localize the subthalamic nucleus from a median of three to two per initial operative side (P < 0.001). The technique also provides the novel opportunity to examine population activity by correlating the discharge between two closely spaced simultaneously recorded neurons and can be used to monitor the electrophysiological effects of local electrical stimulation or microinjections of pharmacological agents. CONCLUSION: Our experience indicates that the use of two closely spaced microelectrodes improves the utility of microelectrode localization in minimally invasive functional neurosurgery.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".