Systematic review with meta-analysis: cytokines in fibromyalgia syndrome
Bibliographic record
Abstract
BACKGROUND: To perform a systematic review and meta-analysis on cytokine levels in patients with fibromyalgia syndrome (FMS). METHODS: Through December 2010 we systematically reviewed the databases PubMed, MEDLINE, and PsycINFO and screened the reference lists of 22 review articles for suitable original articles. Original articles investigating cytokines in patients with FMS were included. Data were extracted by two independent authors. Differences of the cytokine levels of FMS patients and controls were summarized by standardized mean differences (SMD) using a random effects model. Study quality was assessed applying methodological scores: modified Center of Evidence Based Medicine, Newcastle-Ottawa-Scale, and Würzburg Methodological Quality Score. RESULTS: Twenty-five articles were included investigating 1255 FMS patients and 800 healthy controls. Data of 13/25 studies entered meta-analysis. The overall methodological quality of studies was low. The results of the majority of studies were not comparable because methods, investigated material, and investigated target cytokines differed. Systematic review of the selected 25 articles revealed that FMS patients had higher serum levels of interleukin (IL)-1 receptor antagonist, IL-6, and IL-8, and higher plasma levels of IL-8. Meta-analysis of eligible studies showed that FMS patients had higher plasma IL-6 levels compared to controls (SMD = -0.34 [-0.64, -0.03] 95% CI; p = 0.03). The majority of investigated cytokines were not different between patients and controls. CONCLUSIONS: The pathophysiological role of cytokines in FMS is still unclear. Studies of higher quality and with higher numbers of subjects are needed.
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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 | 0.044 | 0.098 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.041 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".