Dimensions of alexithymia and their links to anxiety and depression
Bibliographic record
Abstract
Introduction Anxiety and depression are among the most common psychiatric comorbidities in multiple sclerosis (MS) patients. These disorders could lead to significant emotional disturbances. Objectives To study the different dimensions of alexithymia in patients with MS and determine their relationship with anxiety and depression. Methods Our study, descriptive and analytical, focused on patients followed for MS at the neurology department in Sfax (Tunisia). In addition to collecting sociodemographic data, we used the Hospital Anxiety and Depression Scale (HADS) to assess anxiety and depressive symptoms and the Toronto Alexithymia Scale (TAS-20) to assess alexithymia and its three dimensions: difficulty identifying emotions (DIE), difficulty differentiating emotions (DDE), and externally oriented thinking (EOT). Results This study included 93 patients followed for MS. Our results showed a prevalence of 58.1% for alexithymia, 38.7% for anxiety and 26.9% for depression. The median score of the dimension DIE was 22. The median score of the dimension DDE was 17. The mean score for the dimension EOT was 26.96 ± 4.18. Alexithymic patients were more anxious and depressed (p = 0,002 and p < 10-3, respectively). Both dimensions DIE and DDE were associated with anxiety (p = 0.001 and p = 0.022, respectively) and depression (p < 10-3 and p < 10-3, respectively). Non-depressed patients had a higher score on the EOT dimension (p = 0.003). Conclusions Our results showed a relationship between depression, anxiety and alexithymia, hence the importance of looking for alexithymia in MS patients with anxiety or depressive symptoms. Disclosure No significant relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".