Alexithymia may modulate decision making \nin patients with de novo Parkinson’s disease
Bibliographic record
Abstract
The aim of this study was to investigate whether and \nhow alexithymia may influence decision making under \nconditions of uncertainty, assessed using the Iowa \nGambling Task, in patients with newly diagnosed, untreated \n(de novo) Parkinson’s disease, as previously reported \nfor healthy subjects. \nTwenty-four patients with de novo Parkinson’s disease \nunderwent a neuropsychological and neuropsychiatric \nassessment, including the Toronto Alexithymia Scale, \nthe Geriatric Depression Scale Short Form, and the \nIowa Gambling Task (IGT). \nThe assessment showed that 12 patients were alexithymic \nand 12 were non-alexithymic; seven patients \nwere found to be mildly depressed and 17 non-depressed. \nAlexithymic and non-alexithymic patients did \nnot differ in the IGT total score; however, significant differences \nemerged across the third block of the IGT, in \nwhich the alexithymic patients outperformed the nonalexithymic patients. Depression did not influence IGT \nperformance. \nAlexithymia may modulate decision making, as assessed \nwith the IGT; alexithymia could be associated with faster learning to avoid risky choices and negative feedback, as previously reported in some studies conducted in anxious or depressed patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".