Does Late-Onset Huntington Disease Represent a Distinct Symptomatic Picture? Evidence for a Selective Deficit in Executive Function and Emotion Recognition, in the Absence of Behavioral and Psychiatric Disorders
Bibliographic record
Abstract
Huntington Disease (HD) is an autosomal-dominant, neurodegenerative disorder, including motor, cognitive, emotional and behavioral symptoms. Motor symptoms used to set the clinical onset, typically emerge in the middle age. Here, we describe the case of a patient, who received a genetic diagnosis at 75 years and developed motor symptoms at 80. The Patient shows severe motor symptoms in the absence of personality changes or psychiatric disorders typically observed in HD. For what attain neuropsychological profile, it results unaltered apart from a specific deficit in emotion recognition and general slowness on executive functioning tasks, reflecting a specific trade-off between accuracy and rate of performances, that is a selective impairment in fine-tuning of resources. Both of these deficits in the Patient could be ascribable to the frontostriatal atrophy, evidenced by Computed Tomography. While deficit in emotion recognition is a well-known symptom in HD, a deficit in fine-tuning of resources regards a specific aspect of executive function. The ability of fine-tuning resources is the latest step in the development of executive functions, and it could be also the first level to be impaired in HD. We proposed that deficit in fine-tuning of resources could be the core of the neuropsychological deficit in late-onset 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".