Prevalence and Incidence of Huntington's Disease: An Updated Systematic Review and Meta‐Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The incidence and prevalence of Huntington's disease (HD) based on a systematic review and meta-analysis of 20 studies published from 1985 to 2010 was estimated at 0.38 per 100,000 person-years (95% confidence interval [CI], 0.16-0.94) and 2.71 per 100,000 persons (95% CI, 1.55-4.72), respectively. Since 2010, there have been many new epidemiological studies of HD. We sought to update the global estimates of HD incidence and prevalence using data published up to February 2022 and perform additional analyses based on study continent. Medline and Embase were searched for epidemiological studies of HD published between 2010 and 2022. Risk of bias was assessed using a quality assessment tool. Estimated pooled prevalence or incidence was calculated using a random-effects meta-analysis. A total of 33 studies published between 2010 and 2022 were included. Pooled incidence was 0.48 cases per 100,000 person-years (95% CI, 0.33-0.63). Subgroup analysis by continent demonstrated a significantly higher incidence of HD in Europe and North America than in Asia. Pooled prevalence was 4.88 per 100,000 (95% CI, 3.38-7.06). Subanalyses by continent demonstrated that the prevalence of HD was significantly higher in Europe and North America than in Africa. The minor increase in prevalence (more so than incidence) demonstrated in this updated review could relate to the enhanced availability of molecular testing, earlier diagnosis, increased life expectancy, and de novo mutations. Limitations include variable case ascertainment methods and lacking case validation data. © 2022 Her Majesty the Queen in Right of Canada. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. Reproduced with the permission of the Minister of Public Health Agency of Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it