Modeling Manifest Huntington’s Disease Prevalence Using Diagnosed Incidence and Survival Time
Bibliographic record
Abstract
INTRODUCTION: Understanding the epidemiology of Huntington's disease (HD) is key to assessing disease burden and the healthcare resources required to meet patients' needs. We aimed to develop and validate a model to estimate the diagnosed prevalence of manifest HD by the Shoulson-Fahn stage. METHODS: A literature review identified epidemiological data from Brazil, Canada, France, Germany, Italy, Spain, the UK, and the USA. Data on staging distribution at diagnosis, progression, and mortality were derived from Enroll-HD. Newly diagnosed patients with manifest HD were simulated by applying annual diagnosed incidence rates to the total population in each country, each year from 1950 onwards. The number of diagnosed prevalent patients from the previous year who remained in each stage was estimated in line with the probability of death or progression. Diagnosed prevalence in 2020 was estimated as the sum of simulated patients, from all the incident cohorts, still alive. RESULTS: The model estimates that in 2020, there were 66,787 individuals diagnosed with HD in the 8 included countries, of whom 62-63% were in Shoulson-Fahn stages 1 and 2 (with less severely limited functional capacity than those in stages 3-5). Diagnosed prevalence is estimated to be 8.2-9.0 per 100,000 in the USA, Canada, and the 5 included European countries and 3.5 per 100,000 in Brazil. CONCLUSION: The modeled estimates generally accord with the previously published data. This analysis contributes to better understanding of the epidemiology of HD and highlights areas of uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".