OS12 - 137 <b>The Geriatric Scoring System (GSS) for Risk Stratification in Meningioma Patients as a Predictor of Outcome in Patients Treated With Radiosurgery</b>
Bibliographic record
Abstract
Meningiomas are the most common primary benign brain tumor. Radiosurgery (primary or adjuvant) allows excellent local control. The Geriatric scoring system (GSS) for pre-operative risk stratification and outcome prediction of patients with meningiomas has been previously reported. The GSS incorporates eight tumor and patient parameters on admission. A GSS score higher than 16 was previously reported to be associated with a more favorable outcome. We assessed the validity of the GSS score and its influence on outcome in patients treated with gamma-knife radiosurgery (GKRS). Methods: Patients treated with single session GKRS for WHO-1 meningioma during 1989-2013 at the University of Virginia were reviewed. A cohort of 323 patients, 50.2% (n=162) males. Median age was 56 (29-84), and median follow-up was 53.6 (6-235) months. Median tumor volume was 4.5 cm3 (0.2-23). Median margin and maximal doses were 15 Gy (8-36) and 32.3 Gy (20-65), respectively. Results: Tumor volume control was achieved in 87% (n=281), and post-GKRS clinical neurological improvement reported in 66.3% (n=214). The median change in KPS was +10 (range -30 to +40). The most common complication was intermittent headaches (34.1%, n=110) and cranial nerve deficits (14.2%, n=46). The GSS (calculated and grouped as GSS>16 and GSS<=16) was found to correlate with different Post-GKRS functional status (p<0.0001) and tumor control (p=0.028). Conclusion: The GSS, used for risk stratification and outcome prediction in patients with meningiomas seems valid for patients undergoing single session GRKS. GSS score greater than 16 is associated with a better long-term functional status and tumor control.
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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 | 0.000 | 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) | 0.001 | 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".