Factors explaining variance in perceived pain in women with fibromyalgia
Bibliographic record
Abstract
BACKGROUND: We hypothesized that a substantial proportion of the subjectively experienced variance in pain in fibromyalgia patients would be explained by psychological factors alone, but that a combined model, including neuroendocrine and autonomic factors, would give the most parsimonious explanation of variance in pain. METHODS: Psychometric assessment included McGill Pain Questionnaire, General Health Questionnaire, Hospital Anxiety and Depression Rating Scale, Eysenck personality Inventory, Neuroticism and Lie subscales, Toronto Alexithymia Scale, and Multidimensional Health Locus of Control Scale and was performed in 42 female patients with fibromyalgia and 48 female age matched random sample population controls. A subgroup of the original sample (22 fibromyalgia patients and 13 controls) underwent a pharmacological challenge test with buspirone to assess autonomic and adrenocortical reactivity to serotonergic challenge. RESULTS: Although fibromyalgia patients scored high on neuroticism, anxiety, depression and general distress, only a minor part of variance in pain was explained by psychological factors alone. High pain score was associated with high neuroticism, low baseline cortisol level and small drop in systolic blood pressure after buspirone challenge test. This model explained 41.5% of total pain in fibromyalgia patients. In population controls, psychological factors alone were significant predictors for variance in pain. CONCLUSION: Fibromyalgia patients may have reduced reactivity in the central sympathetic system or perturbations in the sympathetic-parasympathetic balance. This study shows that a biopsychosocial model, including psychological factors as well as factors related to perturbations of the autonomic nervous system and hypothalamic-pituitary-adrenal axis, is needed to explain perceived pain in fibromyalgia 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".