Biopsy versus partial versus gross total resection in older patients with high-grade glioma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Optimal extent of surgical resection (EOR) of high-grade gliomas (HGGs) remains uncertain in the elderly given the unclear benefits and potentially higher rates of mortality and morbidity associated with more extensive degrees of resection. METHODS: We undertook a meta-analysis according to a predefined protocol and systematically searched literature databases for reports about HGG EOR. Elderly patients (≥60 y) undergoing biopsy, subtotal resection (STR), and gross total resection (GTR) were compared for the outcome measures of overall survival (OS), postoperative karnofsky performance status (KPS), progression-free survival (PFS), mortality, and morbidity. Treatment effects as pooled estimates, mean differences (MDs), or risk ratios (RRs) with corresponding 95% confidence intervals (CIs) were determined using random effects modeling. RESULTS: A total of 12 607 participants from 34 studies met eligibility criteria, including our current cohort of 211 patients. When comparing overall resection (of any extent) with biopsy, in favor of the resection group were OS (MD 3.88 mo, 95% CI: 2.14-5.62, P < .001), postoperative KPS (MD 10.4, 95% CI: 6.58-14.22, P < .001), PFS (MD 2.44 mo, 95% CI: 1.45-3.43, P < .001), mortality (RR = 0.27, 95% CI: 0.12-0.61, P = .002), and morbidity (RR = 0.82, 95% CI: 0.46-1.46, P = .514) . GTR was significantly superior to STR in terms of OS (MD 3.77 mo, 95% CI: 2.26-5.29, P < .001), postoperative KPS (MD 4.91, 95% CI: 0.91-8.92, P = .016), and PFS (MD 2.21 mo, 95% CI: 1.13-3.3, P < .001) with no difference in mortality (RR = 0.53, 95% CI: 0.05-5.71, P = .600) or morbidity (RR = 0.52, 95% CI: 0.18-1.49, P = .223). CONCLUSIONS: Our findings suggest an upward improvement in survival time, functional recovery, and tumor recurrence rate associated with increasing extents of safe resection. These benefits did not result in higher rates of mortality or morbidity if considered in conjunction with known established safety measures when managing elderly patients harboring HGGs.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".