Age and Sex Disparities in Latin-American Adults with Gliomas: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Objective : This study aimed to identify if there are ethnic differences in the age and sex distribution of gliomas in the Latino adult population. Methods: A systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 recommendations. Databases used were MEDLINE, LILACS, Web of Science, and Scopus. Studies were included if they reported the age and/or sex distribution of gliomas in Latin adults, published in English or Spanish from January 1st, 1985, to December 1st, 2022. The quality of the studies was assessed using the Newcastle - Ottawa Quality Assessment Scale and the NIH Quality Assessment Tool [1] Results: From 1,096 articles, fifteen studies with information on 6,815 patients were selected for the systematic review, and thirteen were selected for the meta-analysis. The mean ages of diagnosis of glioma and glioblastoma were 50.9, 95\%\ CI [47.8 - 53.9] years and 53.33 years, 95 \% CI [51-55.6], respectively. The male-to-female incidence rate ratio of gliomas was 1.39. Conclusion: Our study found mean ages of glioma and glioblastoma were 6 and 10 years lower than those reported in the CBTRUS. Our study suggests disparities in the age and sex distribution of gliomas in Latin America compared to other regions. PROSPERO registration number: CRD42021274423
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".