SURG-15. INTEGRATING MOLECULAR MARKERS AND EXTENT OF RESECTION FOR RISK STRATIFICATION OF PATIENTS WITH NEWLY-DIAGNOSED GLIOBLASTOMA: A MULTICENTRE STUDY
Bibliographic record
Abstract
Multiple studies have shown that extent of surgical resection (EoR) is an independent prognostic factor for patients with newly-diagnosed glioblastoma. Previous work has proposed the inclusion of EoR in a risk stratification algorithm but does not incorporate recent advances in the molecular characterization of tumors. To develop an integrative risk stratification scheme that incorporates clinically relevant molecular data and EoR with classic prognostic variables to individualize prognosis and guide treatment and research. We reviewed all consecutive cases of confirmed newly-diagnosed glioblastoma who were operated upon between January 1, 2012 and December 31, 2014 at two tertiary academic centres. Variables including age, sex, KPS, tumour location, presenting symptoms, treatment history, dates of progression and reoperation, as well as MGMT promoter methylation (MGMT-M), IDH, 1p/19q codeletion, and ATRX status were recorded. Computer-assisted volumetric analysis of pre- and post-operative MRIs allowed calculation of pre-operative tumour burden, residual disease, and %EoR. Preliminary results from review of 63 out of 297 cases diagnosed during the study period showed patients with EoR ≥ 95% and positive MGMT-M had the longest median overall survival (23.7 months), but the benefit of MGMT-M was not seen at lower EoR. The combination of MGMT-M and EoR ≥ 95% is synergistic in improving patient survival, possibly reflecting the improved efficacy of chemotherapy at lower residual tumour volumes. Review of remaining cases and recursive partitioning analysis(RPA) are pending, and will allow development of an integrative risk stratification algorithm.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".