P.137 Long term outcome of central neurocytoma: a multicenter study of eleven tertiary care centers
Bibliographic record
Abstract
Background: Due to the peculiar location of central neurocytoma, gross total resection is often challenging, and adjuvant radiotherapy is sometimes indicated. Factors associated with outcome are not well addressed in the literature Methods: A multicenter retrospective cohort study among 11 centers in Saudi Arabia including histologically confirmed central neurocytoma between 2000-2022. Results: A total of 104 patients were included. Pre-operative mRS was 0-2 in 93% of the cases. Gross total resection (GTR) was achieved in 50.7%. Radiotherapy was needed in 40% and Ventriculoperitoneal shunt in 38.6%. Favorable outcome was observed in 81.7%. Factors associated with worse outcome included initial mRs of ≥3, tumor size ≥50 mm, and atypical/anaplastic histology. Disease progression was observed in 18 (17.8%). Five-year absolute survival rate was 91.55%. The only significant predictor of disease progression was the presence of atypical features or anaplasia (P .007). Extent of surgical resection (P 0.877) wasn’t associated with disease progression. Post operative radiotherapy didn’t alter residual progression in patients with subtotal resection (P .170) nor the recurrence rate in those with GTR (.365) Conclusions: Central Neurocytoma cases have a high survival rate with a favourable mRs score at long-term follow up. The extent of resection didn’t alter disease progression nor did the adjuvant radiotherapy
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".