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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".