Intra-operative electrooculographic monitoring to prevent post-operative extraocular motor nerve dysfunction during skull base surgeries
Bibliographic record
Abstract
BACKGROUND AND AIMS: Intra-operative identification and preservation of extraocular motor nerves is one of the main goals of surgeries for skull base tumours and this is done by monitoring the extraocular movement (EOM). Intra-operative electromyographic monitoring has been reported, but it is a complex and skilful process. Electrooculography (EOG) is a simple and reliable technique for monitoring EOMs. We aimed to assess the utility of EOG monitoring in preventing extraocular motor nerve dysfunction during skull base surgeries. METHODS: In this retrospective cohort study, intra-operative EOG recordings were obtained using disposable needle electrodes placed on the periorbital skin and the polarity of the waves noted for interpretation. Triggered as well as continuous EOG responses were recorded after monopolar electrode stimulation of cranial nerve (CN) during tumour removal which helped the surgeon with careful dissection and avoiding potential nerve injuries. RESULTS: Of the 11 cases monitored, oculomotor and abducent nerves were identified in all cases, but the trochlear nerve could not be definitively identified. Six patients had no pre- or post-operative extraocular motor nerve dysfunction. The other five patients had pre-existing deficits before surgery, which recovered completely in two, significantly in one, and did not improve in two patients at 3-6 months follow-up. CONCLUSIONS: EOG was found to be a simple and reliable method of monitoring extraocular motor nerves (CNs III and VI) intraoperatively.
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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 | 0.002 |
| 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".