The worldwide incidence and prevalence of primary brain tumors: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Primary brain tumors are a heterogeneous group of benign and malignant tumors arising from the brain parenchyma and its surrounding structures. The epidemiology of these tumors is poorly understood. The aim of our study is to systematically review the latest literature on the incidence and prevalence of primary brain tumors. METHODS: The systematic review and meta-analysis were conducted according to a predetermined protocol and established guidelines. Only studies reporting on data from 1985 onward were included. Articles were included if they met the following criteria: (i) original research, (ii) population based, (iii) reported an incidence or prevalence estimate of primary brain tumors. RESULTS: From the 53 eligible studies overall, 38 were included in the meta-analysis. A random-effects model found the overall incidence rate of all brain tumors to be 10.82 (95% CI: 8.63-13.56) per 100 000 person-years. The incidence proportion estimates were heterogeneous, even among the same tumor subtypes, and ranged from 0.051 per 100 000 (germ cell tumors) to 25.48 per 100 000 (all brain tumors). There were insufficient data to conduct a meta-analysis of the prevalence of primary brain tumors. CONCLUSIONS: There is a need for more accurate and comparable incidence and prevalence estimates of primary brain tumors across the world. A standardized approach to the study of the epidemiology of these tumors is needed to better understand the burden of brain tumors and the possible geographical variations in their incidence.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".