Psychophysical distress and alexithymic traits in chronic fatigue syndrome with and without comorbid depression
Bibliographic record
Abstract
Patients with chronic fatigue syndrome (CFS) often report a comorbid depressive disorder. Comorbid depression may negatively influence the long-term outcome of CFS therefore it must be correctly diagnosed and treated. The aim of the present study is to provide a clinical and psychometric assessment of CFS patients with and without depressive features. A comparative analysis between 57 CFS subjects (CDC, 1994), 17 of whom with a comorbid depression, and 55 matched healthy volunteers was assessed to evaluate the presence of any psychophysical distress and alexithymic traits, by means of Symptom Checklist-90-R (SCL-90R) and Toronto Alexithymia Scale (TAS-20). The severity of fatigue was also assessed in all CFS patients using the Fatigue Impact Scale (FIS). With regard to psychiatric comorbidity, the SCL-90R scores showed higher levels of somatic complaints in CFS patients than in healthy subjects, whereas augmented depressive and obsessive-compulsive symptoms were observed only in the depressed CFS subgroup. When comparing the TAS-20 scores, we observed a selective impairment in the capacity to identify feelings and emotions, as measured by the Difficulty in Identifying Feelings subscale (DIF), non-depressed CFS patients showing an intermediate score between depressed CFS and healthy controls. Finally, in terms of FIS scores, a statistical trend versus a higher fatigue severity in depressed CFS patients, with respect to non-depressed ones, was observed. In conclusion, comorbid depression in CFS significantly increased the level of psychophysical distress and the severity of alexithymic traits. These findings suggest an urgent need to address and treat depressive disorders in the clinical care of CFS cases, to improve social functioning and quality of life in such 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".