P.165 Surgery for recurrent GBM: deciding when to operate
Bibliographic record
Abstract
Background: Previous studies have found conflicting results regarding the role of repeat surgery on overall survival (OS) in patients with GBM. We used a novel approach that includes time to tumour recurrence as an additional prognostic factor in order to determine which patients benefit most from repeat surgery. Methods: A retrospective chart review from 1992-2018 was performed on all adult (≥ 18 years old) patients with primary GBM that received surgery for recurrent disease and compared to publicly available data from The Cancer Genome Atlas (TCGA) of adult patients with primary GBM that did not undergo surgery for recurrent disease. Results: A total of 672 adult patients with GBM were included in the study, including 87 that received surgery at tumour recurrence (surgery cohort). The surgery cohort had longer OS and similar complication rates to those that did not receive surgery at recurrence, independent of time to tumour recurrence (p < 0.0001 and p = 0.4, respectively). Within the surgery cohort, patients with tumour recurrence >6 months demonstrated additional survival benefit (p < 0.0001). Conclusions: Surgery for recurrent GBM leads to improved survival without increased complications. Patients with tumour recurrence >6 months benefit most from repeat surgery.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".