Commonalities and differences in abnormal peripheral metabolites between patients with fibromyalgia and complex regional pain syndrome
Bibliographic record
Abstract
Background: Fibromyalgia (FM) and complex regional pain syndrome (CRPS) share many pathological mechanisms related to chronic pain that could contribute to multifactorial pathological mechanisms. Methods: We investigated peripheral metabolites in FM and CRPS patients compared to healthy controls based on cross-sectional study. Results: Mean corpuscular hemoglobin (p p = 0.014), and total bilirubin levels (p = 0.017) were lower in FM patients than in healthy controls. On the other hand, CRPS patients showed lower levels of total bilirubin than healthy controls (p = 0.037). Creatinine level was lower in FM patients (p = 0.057) compared to healthy controls, particularly when comparing the low-hemoglobin subgroup among FM patients (p = 0.035) with the low-hemoglobin subgroup among healthy controls. Red blood cell count (r = –0.620, p = 0.031), hematocrit (r = –0.593, p = 0.042), and creatinine level (r = –0.598, p = 0.040) showed negative correlations with McGill Pain Questionnaire-Affective (MPQ-A) scores in FM patients. A negative correlation was observed between MCV and McGill Pain Questionnaire-Sensory scores (r = –0.680, p = 0.015) in CRPS patients. Conclusion: We found specific peripheral metabolites that may exhibit different tendency between FM and CRPS patients as well as some common metabolites, which may be associated with peripheral pathology in the patients. Considering this study had a few limitations such as a small sample sizes and using a liberal threshold of significance in the correlation analysis, future studies with larger sample sizes may be needed to generalize these findings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".