Time will tell: Decision making in premanifest and manifest Huntington’s disease
Bibliographic record
Abstract
OBJECTIVE: To investigate cognitive flexibility in premanifest and manifest Huntington's disease (HD). BACKGROUND: HD is an autosomal dominant neurodegenerative disease characterized by motor, cognitive, and behavioral abnormalities with typical motor symptoms. In this study, we wanted to assess decision making in premanifest (pre-HD) and manifest HD patients. METHODS: A total of 77 non-demented subjects including 29 pre-HD, 22 manifest HD patients, and 26 healthy controls (HC) were included. We stratified the pre-HD group based on their estimated years to disease onset into a far (FAR, n = 13) and a near (NEAR, n = 16) group. Furthermore, participants performed the Montreal cognitive assessment battery (MoCA), the trail making task part A and B (TMT A, TMT B), the Symbol digit modalities test (SDMT), and the beads task. RESULTS: In the beads task, HD patients gathered less information than all other groups (all p-values < .001). Furthermore, the NEAR group gathered less information than the FAR group (p < .001) and HC (p = .001). There was no difference between the HC and the FAR group (p = 1.0). In the TMT and the SDMT, HD patients were slower than all other groups (all p-values < .01) but there were no other significant differences. CONCLUSIONS: Decision making with a higher degree of uncertainty may be an early neuropsychological sign to indicate the disease process prior to reaching criteria for motor diagnosis of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".