Bibliographic record
Abstract
Participants with the gene expansion for Huntington disease (HD) but not yet diagnosed were evaluated annually. Unidimensional diagnosis (UD) was a motor diagnosis defined as a diagnostic confidence level (DCL) of 4 (unequivocal motor signs, ≥99% confidence) on the standardized motor exam of the Unified Huntington Disease Rating Scale (UHDRS). Multidimensional diagnosis (MD) was defined as answering yes on Question 80 (Q80) of the UHDRS, ≥99% confidence of manifest HD based on the entire UHDRS. Motor, cognitive, and behavioral measures of phenotype at first diagnosis were compared by t-tests between participants diagnosed via motor exam (UD) and those diagnosed via multidimensional input (MD). Cluster analysis identified clusters based on UHDRS domains.186 participants received a diagnosis of HD during a maximum of 6.4 years of follow-up. In 108 (58.1%) the diagnosis by MD and UD occurred simultaneously, while in 69 (37.1%) the diagnosis by MD occurred prior to UD. Participants who were diagnosed by MD prior to UD were less impaired on motor (12.2 ± 6.7 vs. 22.4 ± 9.3, p < 0.0001), and cognitive (290.7 ± 56.2 vs. 258.0 ± 53.7, p = 0.0002), but not behavioral measures (16.3 ± 21.2 vs. 18.6 ± 22.1, p = 0.49) when compared with those diagnosed simultaneously. Cluster analysis identified three clusters that represented primarily cognitively impaired, behaviorally impaired, and cognitively preserved phenotypes. A multidimensional method results in an earlier diagnosis with less motor and cognitive impairment than a motor diagnosis. Findings have implications for designing preventive trials and providing clinical care in prodromal 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".