Epidemiology of Huntington's Disease in Latin America: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Latin America has played a crucial role in advancing our understanding of Huntington's disease (HD). However, previous global reviews include limited data from Latin America. It is possible that English-based medical search engines may not capture all the relevant studies. METHODS: We searched databases in Spanish, Portuguese, and English. The names of every country in Latin America in English-based search engines were used to ensure we found any study that had molecular ascertainment and provided general epidemiological information or subpopulation data. Additionally, we contacted experts across the region. RESULTS: The search strategy yielded 791 citations; 24 studies met inclusion criteria, representing 12 of 36 countries. The overall pooled prevalence was 0.64 per 100,000 (prediction interval, 0.06-7.22); for cluster regions, it was 54 per 100,000 (95% CI, 34.79-84.92); for juvenile HD, it was 8.7% (prediction interval, 5.12-14.35), and 5.9% (prediction interval, 2.72-13.42) for late-onset HD. The prevalence was higher for Mexico, Peru, and Brazil. However, there were no significant differences between Central America and the Caribbean versus South America. CONCLUSION: The prevalence of HD appears to be similar across Latin America. However, we infer that our findings are underestimates, in part because of limited research and underdiagnosis of HD because of limited access to molecular testing and the availability of neurologists and movement disorders specialists. Future research should focus on identifying pathways to improve access to molecular testing and education and understanding differences among different ancestral groups in Latin America. © 2024 International Parkinson and Movement Disorder Society.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".