Predictors of survival after second surgery for recurrent glioblastoma tumours
Bibliographic record
Abstract
Background: The impact of second surgery on recurrence remains unclear, with few definitive studies to date. This study sought to identify major predictors of survival after second surgery. Methods: A retrospective chart review was conducted for 21 patients who underwent elective surgery for GBM recurrence, at our institution, in the past 6 years. Kaplan Meier was applied to determine the significance of the variables on survival time. The Mann Whitney U test was used to determine whether the median survival time differed significantly between groups, for the factors of interest. Results: Among variables examined, age, less than ≥50 (P=0.04) was significant. Patients younger than 50, had a median survival period of 11.8 months, while patients, age 50 or older, survived a median time of 4.2 months. Though chemotherapy after reoperation was not found to statistically significantly extend survival time on Kaplan-Meier (P=0.08), the median survival time was found to be significantly higher in patients that received chemotherapy (10.6 months) after reoperation, compared with those who did not (3.9 months), using the Mann Whitney U test (P=0.05). Conclusions: These results confirm that younger patients survive longer after second surgery and indicate that a second round of chemotherapy may prolong survival.
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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 | 0.001 | 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) | 0.002 | 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".