Affective and personality disorders in Parkinson's disease premotor phase. Pilot study.
Bibliographic record
Abstract
Neuropsychiatric symptoms are one of the factors determining quality of life among Parkinson's disease patients, but often remain underestimated by clinicians, especially in early disease stage. Objective: to study the clinical features of affective disorders and comorbid personality traits in prodrome of Parkinson's disease (PD) and their effect on treatment adherence. Materials and methods: 29 patients with PD were studied with questionnaires (Toronto alexithymia scale (TAS); Minimult personal questionnaire; DSM-IV criteria (SCID-II); Hospital anxiety and depression scale (HADS); Hamilton Scale for the Assessment of Depression (HAM-D)), and clinical-psychopathological method with objective information collection, neurological examination and neuropsychological testing. Results: Among the patients with PD, personality premorbid features cluster C were most prominent (48% patients). Affective disorders in the prodromal period of PD are dysthymia (13.7%); cyclothymia (6.8%); recurrent depressive disorder (34.4%); and single depressive episode (44.8%). Affective disorders in PD are characterized by late onset, the prevalence of anxiety, asthenic-apathetic symptoms, as well as a high level of alexithymia and somatization. We found the relationship of personality traits and adherence to therapy: the least compliant are histrionic n = 3 (10.3%) and schizotypal n = 2 (6.9%) patients. A high prevalence of dissociative reactions (55%) was noted in response to a diagnosis of PD, which is similar to cancer patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".