Age-specific global epidemiology of hydrocephalus: Systematic review, metanalysis and global birth surveillance
Bibliographic record
Abstract
BACKGROUND: Hydrocephalus is a debilitating disorder, affecting all age groups. Evaluation of its global epidemiology is required for healthcare planning and resource allocation. OBJECTIVES: To define age-specific global prevalence and incidence of hydrocephalus. METHODS: Population-based studies reporting prevalence of hydrocephalus were identified (MEDLINE, EMBASE, Cochrane, and Google Scholar (1985-2017)). Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Two authors reviewed abstracts, full text articles and abstracted data. Metanalysis and meta-regressions were used to assess associations between key variables. Heterogeneity and publication bias were assessed. Main outcome of interest was hydrocephalus prevalence among pediatric (≤ 18 years), adults (19-64 years), and elderly (≥ 65) patients. Annual hydrocephalus incidence stratified by country income level and folate fortification requirements were obtained (2003-2014) from the International Clearinghouse for Birth Defects Surveillance and Research (ICBDSR). RESULTS: Of 2,460 abstracts, 52 met review eligibility criteria (aggregate population 171,558,651). Mean hydrocephalus prevalence was 85/100,000 [95% CI 62, 116]. The prevalence was 88/100,000 [95% CI 72, 107] in pediatrics; 11/100,000 [95% CI 5, 25] in adults; and 175/100,000 [95% CI 67, 458] in the elderly. The ICBDSR-based incidence of hydrocephalus diagnosed at birth remained stable over 11 years: 81/100,000 [95% CI 69, 96]. A significantly lower incidence was identified in high-income countries. CONCLUSION: This systematic review established age-specific global hydrocephalus prevalence. While high-income countries had a lower hydrocephalus incidence according to the ICBDSR registry, folate fortification status was not associated with incidence. Our findings may inform future healthcare resource allocation and study.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".