234 Using functional status to aid interpretation of cUHDRS scores in patients with Huntington’s disease
Bibliographic record
Abstract
The composite Unified Huntington’s Disease Rating Scale (cUHDRS) is a scoring algorithm that combines Total Functional Capacity (TFC), Total Motor Score, Symbol Digit Modalities Test and Stroop Word Reading measures. Our aim was to enhance understanding of cUHDRS scoring by linking to established measures of meaningful daily function and independence in individuals with early-to-moderate-manifest Hunting- ton’s disease (HD). Data from Enroll-HD were evaluated. For patients meeting the reference population for the cUHDRS (manifest HD, TFC 5–13, ≥20 years; N=3,490), cUHDRS score ranges were calculated. Patients were divided into groups around each integer score; for each grouping, the mean HD stage, Independence Scale (IS) score, mean Functional Assessment (FA) score and number of FA items that ≥50% of individuals achieved were calculated. cUHDRS score groupings ranged 3–18 (N=3,484). For patients in the 14–18 cUHDRS score groupings, ≥50% achieved 25 FA items and had a mean IS=95. For patients in the lowest cUHDRS score group, cUHDRS=3, only 12 FA items were achievable by ≥50% and mean IS=65. cUHDRS scores reflect the differing levels of function in individuals with early-to-moderate-manifest HD. The cUHDRS can better differentiate between individuals with Stage 1 HD than commonly used measures of function. rachel.blair@roche.com
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".