Intraoperative monitoring with visual evoked potentials for brain surgeries
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to determine the performance of intraoperative visual evoked potentials (VEPs) in detecting visual field changes. METHODS: Assessments of VEPs were performed with simultaneous retinal responses by using white light-emitting diodes protected from scialytic microscope lights. The alarm criterion was a reproducible decrease in amplitude of the VEP P100 wave of 20% or more. Visual fields were assessed preoperatively and 1 month postsurgery (Goldmann perimetry). RESULTS: The VEPs were analyzed for 29 patients undergoing resection of a brain lesion. In 89.7% of patients, steady VEP and retinal responses were obtained for monitoring. The absence of alarm was associated in 94.4% of cases with the absence of postoperative visual changes (specificity). The alarms correctly identified 66.7% of cases with any postoperative changes and 100% of cases with changes more severe than just a discrete quadrantanopia or deterioration of an existing quadrantanopia (sensitivity, new diffuse deterioration < 2 dB). In 11.5% of patients, a transitory VEP decrease with subsequent recovery was observed without postoperative defects. CONCLUSIONS: Intraoperative VEPs were performed with simultaneous recording of electroretinograms, with protection from lights of the operating room and with white light-emitting diodes. Intraoperative VEPs were shown to be reliable in predicting postoperative visual field changes. In this series of intraaxial brain procedures, reliable intraoperative VEP monitoring was achieved, allowing at minimum the detection of new quadrantanopia. The standardization of this technique appears to be a valuable effort in regard to the functional risks of homonymous hemianopia.
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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 | 0.005 |
| 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 | 0.000 | 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".