Multisource ascertainment of Huntington disease in Canada: Prevalence and population at risk
Bibliographic record
Abstract
There is uncertainty surrounding the accuracy of prevalence estimates for Huntington's disease (HD). The aims of this study were to provide a best estimate of the prevalence and population at risk for HD in the province of British Columbia (BC), Canada, in 2012. HD patients with a clinical and/or genetic diagnosis of HD and individuals at risk for HD were ascertained from multiple sources. Clinical and genetic data were obtained from all available medical, social service, and genetic testing records. Six hundred and thirty-one HD patients and 3,763 individuals at 25% or 50% risk for HD were identified. Prevalence of HD was estimated at 13.7 per 100,000 (95% confidence interval [CI]: 12.6-14.8 per 100,000) in the general population, and 17.2 per 100,000 (95% CI: 15.8-18.6 per 100,000) in the Caucasian population. The population at 25% to 50% risk was estimated at 81.6 per 100,000 (95% CI: 79.0-84.2 per 100,000) individuals. These figures suggest there may be up to 4,700 individuals affected with HD and 14,000 at 50% risk for HD in Canada as well as up to 43,000 individuals affected with HD and 123,000 at 50% risk for HD in the United States. This is the first direct assessment of HD epidemiology in Canada in over three decades. These findings suggest that underascertainment may have led to previous underestimates of prevalence, namely, in Caucasian populations, and will aid in the planning of appropriate resource allocation and service delivery for the HD community.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".