Alexithymia as a moderator of affective modulation of symptom reporting in functional somatic syndromes
Bibliographic record
Abstract
Functional somatic syndromes (FSS), such as Chronic Fatigue Syndrome (CFS) and fibromyalgia, are characterized by chronic and debilitating somatic symptoms that are insufficiently explained by structural organic dysfunction. It has been suggested that alexithymia might contribute to the experience of symptoms in FSS because the inability to correctly identify, classify, and interpret emotions is related to an increased confusion between changes in bodily states accompanying negative emotions and changes in bodily states that are a sign of disease. To investigate this, patients with fibromyalgia and/or chronic fatigue syndrome (CFS: N = 81) and healthy controls (HC: N = 41) viewed series of neutral, positive, and negative affective pictures. After every picture series, participants filled out a somatic symptom checklist. Alexithymia was measured with the "difficulty identifying feelings" (DIF) subscale of the Toronto Alexithymia Scale. We found that negative affective states elicited elevated symptom reports in patients experiencing fibromyalgia and/or CFS, but not in healthy individuals. Interestingly, this difference between patients and controls disappeared when controlling for DIF as a full mediator of this effect, indicating that the exaggerated affective modulation of symptom reporting in FSS patients can be explained by higher average levels of alexithymia in FSS 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".