The incidence and prevalence of Huntington's disease: A systematic review and meta‐analysis
Bibliographic record
Abstract
Huntington's disease (HD) is a rare, neurodegenerative disorder characterized by chorea, behavioral manifestations, and dementia. The aim of this study was to estimate the incidence and prevalence of HD through a systematic review of the literature. Medline and Embase databases were searched using terms specific to HD as well as studies of incidence, prevalence, and epidemiology. All studies reporting the incidence and/or prevalence of HD were included. Twenty original research articles were included. Eight studies examined incidence, and 17 studies examined prevalence. Meta-analysis of data from four incidence studies revealed an incidence of 0.38 per 100,000 per year (95% confidence interval [CI]: 0.16, 0.94). Lower incidence was reported in the Asian studies (n = 2), compared to the studies performed in Europe, North America, and Australia (n = 6). The worldwide service-based prevalence of HD, based on a meta-analysis (n = 13 studies), was 2.71 per 100,000 (95% CI: 1.55-4.72). Eleven studies were conducted in Europe, North American, and Australia, with an overall prevalence of 5.70 per 100,000 (95% CI: 4.42-7.35). Three studies were conducted in Asia, with an overall prevalence of 0.40 per 100,000 (95% CI: 0.26-0.61). Metaregression revealed a significantly lower prevalence of HD in Asia, compared to European, North American, and Australian populations. HD is a devastating neurodegenerative disorder with a higher prevalence in Europe, North America, and Australia than in Asia. The difference in prevalence of this genetic disorder can be largely explained by huntingtin gene haplotypes.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.008 | 0.010 |
| 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".