Bibliographic record
Abstract
BACKGROUND: In fibromyalgia, problems of affect regulation are considered important. Alexithymia, too, is related to disturbed affect regulation. Recognising alexithymia is important with regard to the doctor-patient relationship, the pitfalls in this relationship and the therapeutic strategy. AIM: To look into the prevalence of alexithymia in fibromyalgia and find out which measures were used. METHOD: We reviewed the literature systematically using Medline, PubMed and Cochrane and key words. RESULTS: We found 11 relevant studies which revealed a significantly high prevalence of alexithymia in fibromyalgia patients, namely between 15 and 52%, whereas the prevalence in the general population was only 6 to 8%. All of these studies used the Toronto Alexithymia Scale (20-item or 26-item version) as the only test for alexithymia. Male fibromyalgia patients were not examined adequately, nor were patients in a residential setting. Three studies used patients with a painful chronic condition as a control group, but we did not find any studies that involved psychiatric control groups. CONCLUSION: In view of the high prevalence of alexithymia and the implications of this for therapy, we recommend that patients with fibromyalgia should be screened systematically for alexithymia. Further research involving male patients and residential fibromyalgia patients is required and future studies will have to include psychiatric control groups.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".