The Relationship between Alexithymia and General Symptoms of Patients with Depressive Disorders
Bibliographic record
Abstract
OBJECTIVE: Depression has been associated with alexithymic features. However, few studies have investigated the differences in the general symptoms of patients with depressive disorders according to the presence of alexithymia. Thus, the aim of this study was to evaluate the relationship between alexithymia and symptoms experienced by patients with clinically diagnosed depressive disorders. METHODS: A chart review of patients who were evaluated using the Korean version of the 20-item Toronto Alexithymia Scale (TAS-20) and Symptom Checklist 90-Revised (SCL-90-R) at the same time between the years 2003 and 2007 was conducted. A total of 104 patients with depressive disorders were included and divided into two groups: alexithymia (n=52) and non-alexithymia (n=52). A direct comparison between the two groups was carried out. Regression analysis was also carried out for the TAS-20 total and subset scores in order to model the relationship between alexithymia and symptoms. RESULTS: The presence of alexithymia was confirmed in 50% of the patients with depressive disorders, and the symptoms of depressive patients with alexithymia were more severe than those of their non-alexithymic counterparts on all 9 symptom domains of the SCL-90-R. Furthermore, regression analysis revealed that the presence of alexithymia was positively associated with depression, phobic anxiety, and psychoticism but inversely associated with anxiety. CONCLUSION: These results suggest that the clinical features of depression are partially dependent on the presence of alexithymia. Alexithymic patients with depressive disorders are likely to show more severe depressive, psychotic, and phobic symptoms. In other words, clinicians should suspect the presence of alexithymic tendencies if these symptoms coexist in patients with depressive disorders and address their difficulties in effective communication.
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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 | 0.003 |
| 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 | 0.000 | 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".