NCOG-28. SURGERY FOR RECURRENT GBM IMPROVES SURVIVAL INDEPENDENT OF PROGNOSTIC FACTORS
Bibliographic record
Abstract
Abstract BACKGROUND The survival benefit of surgery for recurrent GBM in the current literature is variable, and often attributed to improvements in surgical technique over time. Studies examining outcomes over prolonged periods are lacking. The primary objective of our study was to determine if surgery for recurrent GBM confers a survival benefit compared to patients not undergoing repeat surgery. The secondary objective of our study was to investigate the effect of known prognostic factors on survival for patients undergoing repeat surgery for GBM. METHODS We analyzed our brain tumour biobank database from 1992 to 2018 for all adult patients that underwent repeat surgery for primary recurrent GBM. This cohort was compared to publicly available data from The Cancer Genome Atlas (TCGA). Survival and prognostic factors were compared within and between groups using Kaplan-Meier survival curves and Cox proportional hazards models. RESULTS A total of 676 patients were included (surgery cohort = 91, non-surgery cohort = 585). The surgery group had a lower age at diagnosis (50.6 vs 58.2 years old, p = < 0.001) and higher Karnofsky Performance Status (KPS) (81.4 vs 77.5, p = 0.002) at diagnosis than the non-surgery group. Other known prognostic factors including IDH status and MGMT methylation status were balanced between groups. Overall median survival was higher in the surgery group than the non-surgery group (30.6 months vs 13.9 months, p = < 0.0001). Within the surgery group, prognostic factors including age, sex, KPS, IDH status, MGMT status, extent of resection by surgeon, tumour location, and time to recurrence were not associated with additional survival benefit. CONCLUSIONS Patients that undergo surgery for recurrent GBM are of younger age and lower KPS at time of initial diagnosis. In patients that undergo repeat surgery, known patient and tumour prognostic factors are not associated with additional survival benefit.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".