P.141 Endoscopic transorbital approach to the skull base: a single centre experience
Bibliographic record
Abstract
Background: Minimally invasive endoscopic techniques via the transorbital approach (ETOA) have emerged as a promising alternative for addressing skull base tumours. This study aims to showcase our institution’s extensive experience with ETOA, detailing the surgical technique employed and presenting comprehensive patient outcomes. Methods: A retrospective analysis was conducted on data from patients who underwent ETOA within the past five years. Results: Over the study period, 24 ETOA procedures were performed on 21 patients, with an average age of 48.92, 13 of whom were women. The superior orbital corridor was utilized in 95.83% of cases, and in 79.17%, ETOA was complemented by a transnasal approach. Spheno-orbital meningioma accounted for the most common surgical indication (33.33%, n=8), all resulting in vision improvement, followed by lateral frontal sinus mucocele (25%, n=6). The median length of stay was one day, and ETOA achieved the procedure goal in 19 patients. Transient V1 numbness was the primary complication (29.17%, n=7), and 20.83% (n=5) necessitated another surgery. Notably, no mortality was associated with this procedure. Conclusions: Our institution’s experience underscores the notable safety and efficacy potential of ETOA, with 19 out of 21 patients exhibiting positive outcomes, obviating the need for revision surgery in most cases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".