Anthropometrics, cancer risks, and survival outcomes in adult patients with glioma – a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose The association between anthropometric measures including BMI, height and cancer has been widely discussed. However, the role of these in the development and prognosis of glioma remains controversial. We aimed to study these relationships. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Papers reporting relationship between anthropometric measures and the risk of glioma, both incidence and survival, were considered relevant. Those published until January 31, 2024, were selected from PubMed, EMBASE, and the Cochrane Library. Studies were evaluated according to the modified Newcastle Ottawa Scale. Hazard ratios, relative risks, and 95% confidence intervals were pooled and synthesized using a random effects model. Results Among 940 screened articles, 23 were selected. Taller height was significantly associated with an increased risk of both glioma (HR per 10 cm, 1.19; CI, 1.16 to 1.23) and glioblastoma (HR per 10 cm, 1.25; CI, 1.18 to 1.31). Higher BMI was positively correlated with an increased risk of glioma, both in categorical (RR, 1.08; CI, 1.03 to 1.12) and continuous measures (HR per 5 kg/m2, 1.01; CI, 1.00 to 1.03). Glioblastoma demonstrated a higher incidence risk (HR per 5 kg/m2, 1.02; 95% CI 1.00 to 1.05) and better survival outcomes (HR 0.75; 95% CI 0.59 to 0.96) with increasing BMI. Conclusion This study provides critical insights into the relationship between glioma and anthropometric measures. Glioma and glioblastoma were associated with these measures in terms of both incidence and survival. Further research is necessary to uncover the mechanisms and develop preventative or therapeutic strategies.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".