Bibliographic record
Abstract
BACKGROUND: The optimal design and outcome measures for preventive clinical trials in neurodegenerative diseases are unknown. OBJECTIVE: To examine measures that may be associated with disease in the largest cohort ever recruited of prediagnosed individuals carrying the gene expansion for Huntington disease (HD). DESIGN: The Predict-HD study is a multicenter observational research study in progress at 17 sites in the United States, 4 in Canada, and 3 in Australia. SETTING: Genetics and HD outpatient clinics. PARTICIPANTS: Five hundred five at-risk individuals who had previously undergone elective DNA analyses for the CAG expansion in the HD gene (predictive testing) and did not currently have a clinical diagnosis of HD. MAIN OUTCOME MEASURES: Basal ganglia volumes on magnetic resonance images, estimated probability of diagnosis (based on CAG repeat length), performances on 21 standardized cognitive tasks, total scores on 3 scales of psychiatric distress, and motor diagnosis based on the Unified Huntington's Disease Rating Scale. RESULTS: Several variables showed progressive decline as the diagnostic ratings advanced toward manifest disease. Estimated probability of diagnosis was associated with Unified Huntington's Disease Rating Scale prediagnostic stages and varied from 15% in persons with no motor abnormalities to nearly 40% in those with abnormalities suggestive of probable disease. Striatal volumes, cognitive performances, and even psychiatric ratings declined significantly with motor manifestations of disease. CONCLUSIONS: The documentation of biological and refined clinical markers suggests several clinical end points for preventive clinical trials. Longitudinal study is critical to further validate possible markers for prediagnosed HD.
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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 | 0.220 | 0.353 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.087 | 0.053 |
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".