Diagnostic criteria for Huntington's disease based on natural history
Bibliographic record
Abstract
Huntington's disease (HD) is currently diagnosed based on the presence of motor signs indicating 99% "diagnostic confidence" for HD. Recent advances in the understanding of HD natural history and neurobiology indicate that disease-related brain changes begin at least 12 to 15 years before the formal diagnosis based on motor onset. Furthermore, subtle motor dysfunction, cognitive changes, and behavioral alterations are often seen before diagnosis made according to the current criteria. As disease-modifying treatments are developed, likely beginning therapy early will be desirable. We therefore suggest that expanded diagnostic criteria for HD should be adapted to better reflect the natural history of the disease, to enable the conduct of clinical trials in premanifest subjects targeting prevention of neurodegeneration, and to facilitate earlier symptomatic treatment. We propose a new set of criteria for HD diagnostic categories in the International Classification of Diseases that reflect our current understanding of HD natural history and pathogenesis. Based on defined criteria, for example, the Diagnostic Confidence Level and the Total Functional Capacity scales of the Unified Huntington's Disease Rating Scale, HD should be divided in the categories "genetically confirmed" with the subcategories "presymptomatic," "prodromal," and "manifest" and "not genetically confirmed" subdivided into "clinically at risk," "clinically prodromal," and "clinically manifest."
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".