The Deficit of Pain Inhibition in Fibromyalgia Is More Pronounced in Patients With Comorbid Depressive Symptoms
Bibliographic record
Abstract
BACKGROUND: On pathophysiologic grounds, fibromyalgia (FM) is characterized by a deficit in diffuse noxious inhibitory controls (DNIC), but the role of depressive symptoms on these mechanisms has not been investigated. We hypothesized that the deficit in pain inhibition would be more pronounced in FM patients with depressive symptoms (FM+D), relative to patients without such symptoms (FM-D). METHODS: Fifty-two women diagnosed with FM (American College of Rheumatology criteria) and 10 healthy women participated in this study. Thermal stimuli were used to measure pain thresholds and DNIC efficacy (spatial summation paradigm). Clinical pain was measured using visual analog scales. RESULTS: We found that the amplitude of DNIC was smaller in FM+D patients, relative to the FM-D group; and that daily pain (unpleasantness) was higher in the FM+D group, relative to FM-D patients. DISCUSSION: We found that FM+D patients have a more pronounced deficit in pain inhibition as well increased clinical pain. As such, these results show the usefulness of combining psychologic factors and psychophysical measures to identify subgroups of FM patients. These results may have implications for future treatment of FM patients with and without comorbid depressive symptoms.
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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".