International Tuberculum Sellae Meningioma Study: Preoperative Grading Scale to Predict Outcomes and Propensity-Matched Outcomes by Endonasal Versus Transcranial Approach
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Tuberculum sellae meningiomas are resected via an expanded endonasal (EEA) or transcranial approach (TCA). Which approach provides superior outcomes is debated. The Magill-McDermott (M-M) grading scale evaluating tumor size, optic canal invasion, and arterial involvement remains to be validated for outcome prediction. The objective of this study was to validate the M-M scale for predicting visual outcome, extent of resection (EOR), and recurrence, and to use propensity matching by M-M scale to determine whether visual outcome, EOR, or recurrence differ between EEA and TCA. METHODS: Forty-site retrospective study of 947 patients undergoing tuberculum sellae meningiomas resection. Standard statistical methods and propensity matching were used. RESULTS: The M-M scale predicted visual worsening (odds ratio [OR]/point: 1.22, 95% CI: 1.02-1.46, P = .0271) and gross total resection (GTR) (OR/point: 0.71, 95% CI: 0.62-0.81, P < .0001), but not recurrence ( P = .4695). The scale was simplified and validated in an independent cohort for predicting visual worsening (OR/point: 2.34, 95% CI: 1.33-4.14, P = .0032) and GTR (OR/point: 0.73, 95% CI: 0.57-0.93, P = .0127), but not recurrence ( P = .2572). In propensity-matched samples, there was no difference in visual worsening ( P = .8757) or recurrence ( P = .5678) between TCA and EEA, but GTR was more likely with TCA (OR: 1.49, 95% CI: 1.02-2.18, P = .0409). Matched patients with preoperative visual deficits who had an EEA were more likely to have visual improvement than those undergoing TCA (72.9% vs 58.4%, P = .0010) with equal rates of visual worsening (EEA 8.0% vs TCA 8.6%, P = .8018). CONCLUSION: The refined M-M scale predicts visual worsening and EOR preoperatively. Preoperative visual deficits are more likely to improve after EEA; however, individual tumor features must be considered during nuanced approach selection by experienced neurosurgeons.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".