RELATIONSHIP BETWEEN ALEXITHYMIA AND PARKINSON’S DISEASE IN A TUNISIAN SAMPLE
Bibliographic record
Abstract
INTRODUCTION: Several psychiatric signs are part of non-motor signs of parkinson’s disease (PD), including alexithymia. OBJECTIVES: The objective of this study is to determine the frequency of alexithymia in patients with PD and to study factors associated with it. METHODS: Descriptive and analytical cross-sectional study collected from patients followed at the neurology consultation of Habib Bourguiba’s University Hospital in Sfax, Tunisia. We used: A sociodemographic, clinicaland therapeutic datasheetincludingthe Hoehn and Yahr motor scalefor the staging of the functional disability associated with PD. The Toronto Alexithymia Scale (TAS-20) with a cutoff score = 61. RESULTS: We recruited 47 patients. The average age was 61.47 years with a sex ratio (M/W) = 1.47. The average age of onset of the disease was 51.97 years. Sleep disorders were present in 51.1% of cases.41 patients (87.23%) were treated with dopa therapy. An Hoehn and Yahr stage ≥ 3 was found in 25.5% of patients. TAS: The mean score was 47.38 and alexithymia frequency was 19.1%. Alexithymia was statistically correlated with the presence of sleep disorders (P=0.023) and with an Hoehn and Yahr stage ≥ 3 (p=0.039).The occurrence of alexithymia was not significantly associated with taking dopatherapy (P= 0.31). CONCLUSIONS: Alexithymia has been quite frequent in patients with PD and associated with motor gravityand sleep disorders. It is considered as a non-motor symptom of the disease that needs to be treated promptly. DISCLOSURE OF INTEREST: None Declared
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".