P.008 Anterior skull base surgery future: intraoperative flash visual evoked potentials a novel technique to lessen intraoperative optic nerves and chiasmal injury
Bibliographic record
Abstract
Background: Optic nerve/chiasmal injury is a devastating outcome that may happen during endoscopic surgery. Additionally, one of the goals of endoscopic skull-base surgery is visual improvement, currently there is limited ability of intraoperative visual pathway monitoring. We examine a novel technique using continuous flash visual evoked potentials (FVEPs). Methods: Eyes were stimulated by light stimulators (3 LEDs on each side, 640 nm peak wavelength, 10 ms pulse width, 3000 mCd of luminous intensity). Uniform illumination was placed over eyelids. Recording electrodes were placed at Oz-Fz. The filter cuts were ≤5 Hz and 100 Hz with amplifier gain 20,000 or 50,000. EEG was recorded. Recordings were correlated to pre and post operative VFs and acuity. Droop in the FVEP was examined in relation to intraoprative events. Results: Thirty patients had FVEPs in addition to other neurophysiologic monitoring. Patients demographic data, co-morbidities, diagnosis, surgical approach, length of surgery, MAP, and blood loss during surgery were recorded. All patients’ visual acuity and field deficits were evaluated by neuro-opthalmologist before their surgery and within 30 days after surgery. Conclusions: FVEP is reproducible throughout surgery and can predict the post surgical outcome. Additionally, we found that FVEP is transiently affected by different stages of surgery. Also boluses of propofol and electrocautery can artificially affect FVEP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".