P.162 The challenge of Giant Olfactory Groove Meningiomas: long-term outcome and predictive modeling
Bibliographic record
Abstract
Background: Giant olfactory groove meningiomas (OGMs), though rare, pose challenges due to their size. These slow-growing tumors often remain asymptomatic until exceeding 6 cm in diameter. While surgery has advanced, understanding long-term outcomes remains crucial. Methods: This retrospective study at a major medical center included all patients with giant OGMs (>6 cm) undergoing resection from 2000-2022.Data on visual status, recurrence, and functional status were collected. Multivariable logistic regression identified predictors of recurrence and functional outcome. Results: Thirty-two patients met the inclusion criteria for this study, with a mean age of 55.8years.The mean follow-up period was 62months. The majority of giant OGMs were classified as WHO grade 1(84.4%).Postoperatively, 19patients demonstrated improvement in visual acuity and visual field deficits.Radiological recurrence was observed in nine patients(28.1%) at a mean follow-up of 56months, with only three requiring reoperations for tumor resection.One patient developed a brain abscess, necessitating reoperation.Multivariable analysis identified patient age, Simpson grade of excision, and WHO grade as significant predictors of recurrence rate. Conclusions: This study demonstrates that surgery can improve visual deficits and functional outcomes. Postoperative outcomes were strongly predicted by age, resection extent, and histological grade. Developing a new predictive scale based on these parameters appears to strongly predict the Giant OGMs Long-Term outcome.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".