37 The impact of repeated surgery on survival for patients with recurrent glioblastoma
Bibliographic record
Abstract
Background: Recurrent glioblastoma portends a poor prognosis and the role of repeat surgery in improving survival remains uncertain. Therefore, we undertook a systematic review and meta-analysis in order to determine if repeat surgical resection provides a meaningful survival benefit for patients with recurrent glioblastoma. Methods: Two independent reviewers searched for articles that reported on overall-survival of patients with recurrent glioblastoma using MEDLINE, Embase, Google Scholar, and Cochrane from January 2000 to 2018. Studies that compared overall survival of patients treated with single surgery compared to repeat surgery in the temozolomide era were included for analysis.Primary outcomes were odds ratio for survival at 6, 12, and 24 months from date of initial diagnosis. Secondary outcomes were ratio odds ratio for survival at 6, 12, and 24 months from date of repeat surgery. The proportions of patients who had the outcomes of interest were pooled using random-effects model. Quality assessment was performed using the Newcastle Ottawa Scale. Heterogeneity across trials was quantified by the I2 statistic. Publication bias was evaluated visually using funnel plots and quantified by the Egger regression. Results: Fourteen articles reporting on 3048 patients were included for analysis. The majority of articles were deemed to be of high quality with Newcastle Ottawa scale greater than 7 points. Pooled analysis showed improved overall survival following repeat surgery at 6- (OR 1.73, 95% CI 1.23-2.45, p<0.05), 12- (OR 1.71, 95% CI 1.20-2.45, p<0.05), and 24-months (OR 2.24,95% CI 1.01-4.95, p<0.05) and from date of initial diagnosis at 6- (OR 8.22, 95% CI 5.23-12.93, p<0.01), 12- (OR 4.16, 95% CI 3.25-5.36, p<0.01), and 24- (2.35, 95% CI 1.77-3.11, p<0.05) months. Conclusions: Repeat surgery for recurrent glioblastoma is associated with a significant survival advantage independent of other salvage therapies that include chemotherapy, radiation, and other antineoplastic regimens.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.026 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".