P.089 Volumetric extent of resection and visual outcomes in pituitary adenoma patients presenting with visual compromise undergoing the endoscopic endonasal approach
Bibliographic record
Abstract
Background: Reporting extent of resection (EOR) in pituitary adenoma (PA) surgery via endoscopic endonasal approaches (EEA) is not standardized. The use of 3-dimensional volumetric analysis is proposed for measurement of tumor volumes and EOR. Their relationship with visual outcomes is explored. Methods: A retrospective analysis of PA patients presenting with visual disturbances and treated surgically via EEA by a single surgeon between 2006 and 2021. The main outcome was visual function at 12 months post-operatively. Results: 142 patients were included. Majority were male, with mean age of 57.1 years. Most (58.2%) presented with bitemporal hemianopsia. The mean tumor size was 11.3 cm 3 . The mean EOR was 84.5% (range 21.5-99.8%), with a mean post-operative tumor volume of 1.9 cm 3 . Visual function improved in 92.2%. Re-resection for visual deterioration was performed in 5.7% of patients, (mean time 2.4 years). No clinical, pathologic, or imaging factors were significantly associated with visual outcome. A significant association was found between EOR and re-resection (mean EOR 66.7% vs 85.6%, p=0.002). Conclusions: For patients with PA presenting with visual deficits, treatment with EEA led to improvement in visual function in the majority of patients, without the need for gross total resection. EOR was significantly associated with the need for re-resection.
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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 | 0.001 |
| 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) | 0.004 | 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".