Fibromyalgia and mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review of the literature on the association between fibromyalgia and mortality and to pool the results in a meta-analysis. METHODS: The authors searched the PubMed, Scopus, and Web of Science databases using the key words 'fibromyalgia' and 'mortality' to identify studies that addressed an association between fibromyalgia and mortality. Original papers that assessed associations between fibromyalgia and mortality (all or specific causes) and provided an effect measure (hazard ratio (HR), standardised mortality ratio (SMR), odds ratio (OR)) quantifying the relationship between fibromyalgia and mortality were included in the systematic review. Of 557 papers that were initially identified using the search words, 8 papers were considered eligible for the systematic review and meta-analysis. We used a Newcastle-Ottawa scale to assess the risk of bias in the studies. RESULTS: The total fibromyalgia group included 188 751 patients. An increased HR was found for all-cause mortality (HR 1.27, 95% CI 1.04 to 1.51), but not for the subgroup diagnosed by the 1990 criteria. There was a borderline increased SMR for accidents (SMR 1.95, 95% CI 0.97 to 3.92), an increased risk for mortality from infections (SMR 1.66, 95% CI 1.15 to 2.38), and suicide (SMR 3.37, 95% CI 1.52 to 7.50), and a decreased mortality rate for cancer (SMR 0.82, 95% CI 0.69 to 0.97). The studies showed significant heterogeneity. CONCLUSIONS: These potential associations indicate that fibromyalgia should be taken seriously, with a special focus on screening for suicidal ideation, accident prevention, and the prevention and treatment of infections.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".