NCOG-10. SURGERY FOR RECURRENT GBM: A RETROSPECTIVE CASE-CONTROL STUDY
Bibliographic record
Abstract
Abstract OBJECTIVE The role of surgery in recurrent GBM remains a controversial topic. The goal of this study was to perform a case-control analysis including time to tumor recurrence as an additional prognostic factor in order to determine which patients benefit most from repeat surgery. METHODS Our brain tumor database was reviewed over a ten-year period for all adult (≥ 18 years old) patients with primary IDH wildtype GBM that received surgery for recurrent disease. These patients were then age, sex, and treatment-matched to case-controls from our institution that received medical therapy for recurrent disease. RESULTS A total of 174 adult patients with GBM were included in the study, 87 patients that received surgery for recurrent GBM (surgery cohort) and 87 patients that did not receive surgery for recurrent GBM (non-surgery cohort). The surgery cohort had longer overall survival (p = 0.0003) and post recurrence survival (p = 0.001) than the non-surgery cohort. When the surgery cohort was split into two groups based on time to tumor recurrence, the long time to recurrence group ( > 6 months) demonstrated significantly increased survival compared to the short time to recurrence group (p < 0.0001). Multivariate analysis of both cohorts demonstrated surgery for recurrent GBM was independently significant after adjusting for age, KPS, and time to tumor recurrence (p < 0.0001). CONCLUSIONS Surgery for recurrent GBM leads to improved survival independent of age, KPS, and time to tumor recurrence. Patients with time to tumor recurrence greater than 6 months benefit most from additional surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".