Independently validated sex-specific nomograms for predicting survival in patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Glioblastoma (GBM) is the most common primary malignant brain tumor. Sex has been shown to be an important prognostic factor for GBM. The purpose of this study was to develop and independently validate sex-specific nomograms for estimation of individualized GBM survival probabilities using data from 2 independent NRG Oncology clinical trials. METHODS: This analysis included information on 752 (NRG/RTOG 0525) and 599 (NRG/RTOG 0825) patients with newly diagnosed GBM. The Cox proportional hazard models by sex were developed using NRG/RTOG 0525 and significant variables were identified using a backward selection procedure. The final selected models by sex were then independently validated using NRG/RTOG 0825. RESULTS: Final nomograms were built by sex. Age at diagnosis, KPS, MGMT promoter methylation and location of tumor were common significant predictors of survival for both sexes. For both sexes, tumors in the frontal lobes had significantly better survival than tumors of multiple sites. Extent of resection, and use of corticosteroids were significant predictors of survival for males. CONCLUSIONS: A sex specific nomogram that assesses individualized survival probabilities (6-, 12- and 24-months) for patients with GBM could be more useful than estimation of overall survival as there are factors that differ between males and females. A user friendly online application can be found here- https://npatilshinyappcalculator.shinyapps.io/SexDifferencesInGBM/ .
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".