Health-related quality of life outcomes of surgery for diffuse glioma: A systematic review and pooled analysis
Bibliographic record
Abstract
Abstract Background Although progress has been made in understanding the effects of adjuvant therapy on health-related quality of life (HR-QoL) in diffuse glioma patients, less is known about the impact of surgical resection. To address this, we conducted a systematic review and pooled quantitative analysis. Methods PubMed, MEDLINE, and Embase were searched for studies measuring HR-QoL before and after surgery for WHO grade 2-4 adult-type diffuse gliomas. Inclusion was limited to prospective cohort studies and trials on adults with ≥1 month of postoperative follow-up. Metric outcomes were assessed with pooled odds, competing risk analysis, and meta-regression using a random effects model. Bias was assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias 2.0 tool. Results Twelve studies comprising 1000 patients were included. The pooled odds of an unfavorable versus favorable HR-QoL change compared to baseline was not significantly different from 1 within 3 months of surgery (0.843, 95% CI, 0.339-2.100), but significantly less than 1 at final follow-up (0.481, 95% CI, 0.260-0.888). The cumulative incidence of favorable HR-QoL change was significantly higher than that of unfavorable change, with the incidence curves separating after 3 months (χ2(1) = 95.0, P < .001). This was attributable to EQ-5D and EORTC QLQ-C30 but not SF-36. Studies with younger patients, more high-grade tumors, and lower gross total resection rates showed worse outcomes. Conclusion Surgical resection can maintain or improve HR-QoL, but patients at risk of deterioration should be identified early. Future studies must carefully select and interpret HR-QoL instruments, as preference-based and non-preference-based tools may lack comparability.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".