Does alexithymia, independent of depressive and anxiety disorders, correlate with the severity of somatic manifestations among patients with medically unexplained physical symptoms?
Bibliographic record
Abstract
Background: Medically unexplained physical symptoms (MUPS) are highly prevalent, frequently co-occurring with psychiatric symptoms such as depression, alexithymia, and anxiety. Objective: To evaluate the relation between depression, anxiety, and alexithymia and severity of somatic symptoms in patients with MUPS. Method: This is a cross-sectional study conducted on 196 patients suffering from MUPS. The patients were recruited from tertiary care internal medicine and neuropsychiatry clinics during the first quarter of 2019. The Patient Health Questionnaire (PHQ-15), (PHQ-9), Generalized Anxiety Disorder (GAD-7), Structured Clinical Interview for DSM-IV Axis I Disorders Clinician Version (SCID-I-CV), and Toronto Alexithymia Scale (TAS-20) were employed to assess somatic severity, depression, anxiety, major mental disorders, and alexithymia in the sample of patients. Results: The results of the study indicate that GAD-7 and PHQ-9 scores had a positive correlation with PHQ-15 ( p < 0.05) in multivariate regression. In contrast, TAS-20 was not independently correlated with PHQ-15. Adding TAS-20 to GAD-7 and PHQ-9 in the equation of the regression model enhances the predictive capacity of the model ( p < 001). Conclusion: The results of the study indicate that only anxiety and depressive symptoms, but not alexithymia, were associated independently with MUPS severity.
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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 | 0.000 | 0.000 |
| 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".