Reoperation in adult patients with recurrent glioblastoma: A matched cohort analysis
Bibliographic record
Abstract
Background: Despite maximal safe cytoreductive surgery and postoperative adjuvant therapies, glioblastoma (GBM) inevitably recurs and leads to deterioration of neurological status and eventual death. There is no consensus regarding the benefit of repeat resection for enhancing survival or quality of life in patients with recurrent GBM. We aimed to examine if reoperation for GBM recurrence incurs a survival benefit as well as examine its complication profile. Methods: We performed a single-center retrospective chart review on all adult patients who underwent resection of supratentorial GBM between January 1, 2008 and December 1, 2013 at our center. Patients with repeat resection were manually matched for age, sex, tumor location, and Karnofsky Performance Status (KPS) with patients who underwent single resection to compare overall survival (OS), and postoperative morbidity. Results: = .221). In addition, repeat surgical resection had a higher rate of postoperative neurological complications compared to the initial surgery. Conclusions: When compared with matched patients who underwent a single surgical resection, patients undergoing repeat surgical resection did not show significant increase in OS and may have incurred more neurological complications related to the repeat resection. Further studies are required to assess which patients would benefit from repeat surgical resection and optimize timing of the repeat resection in selected patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".