High incidence rate and absent family histories in one quarter of patients newly diagnosed with Huntington disease in British Columbia
Bibliographic record
Abstract
The advent of the direct mutation test for Huntington disease (HD) has made it possible to identify a previously unrecognized symptomatic population of HD, including those with an atypical presentation or patients without a family history of HD. The present study investigated the uptake of this test in the province of British Columbia (BC), Canada and assessed the incidence rate and rate of identification of new mutations for HD. All symptomatic individuals residing in BC who were referred for the genetic test for HD between 1993 and 2000 (n=205) were analyzed for CAG expansion, baseline demographics and clinical data, and a family history of HD. A total of 141 (or 68.8%) had a CAG expansion > or =36. Of these, almost one-quarter (24.1%) did not have a family history of HD. An extensive chart review revealed that 11 patients (or 7.8%) had reliable information on both parents (who lived well into old age) and therefore possibly could represent new mutations for HD. This indicates a three to four times higher new mutation rate than previously reported. Our findings also show that the yearly incidence rate for HD was 6.9 per million, which is two times higher than previous incidence studies performed prior to the identification of the HD mutation. We also identified five persons with a clinical presentation of HD but without CAG expansion (genocopies) (2.4%).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".