Three-Dimensional Accuracy of ECOG Strip Electrode Localization Using Coregistration of Preoperative MRI and Intraoperative Fluoroscopy
Bibliographic record
Abstract
BACKGROUND/AIMS: Algorithms that estimate implanted cortical strip electrode coordinates using postoperative skull X-ray coregistration with preoperative magnetic resonance imaging (MRI) have been proposed. However, when cortical strip electrodes are inserted for temporary use and removed prior to closure, intraoperative imaging - either fluoroscopy or computed tomography (CT) - must be substituted. OBJECTIVES: To measure the accuracy of temporarily inserted subdural strip electrode coordinates using intraoperative fluoroscopic coregistration with preoperative MRI compared to intraoperative CT coregistration with preoperative MRI. METHODS: In 5 patients undergoing movement disorder surgery, preoperative MRI was used to generate a three-dimensional cortical surface manually scaled to fit an intraoperative skull fluorogram with an in situ six-contact subdural electrode strip. Individual contact coordinates were estimated using subjacent gyral and sulcal patterns. Estimated coordinates were compared to reference coordinates obtained by preoperative MRI coregistration with intraoperative CT in the same patients. RESULTS: Mean electrode coordinate distances between estimated and reference locations were 6.0 ± 0.8 (x-axis, mediolateral), 3.3 ± 0.5 (y-axis, anterior-posterior) and 4.0 ± 0.5 mm (z-axis, superior-inferior; n = 30). CONCLUSIONS: Localization of temporarily inserted subdural electrodes can be accomplished using preoperative MRI and intraoperative fluoroscopy. The accuracy of this approach is verified by preoperative MRI and intraoperative CT coregistration in the same patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".