Systematic review of the epidemiological burden of Myalgic ncephalomyelitis/Chronic Fatigue Syndrome across Europe: Current evidence and EUROMENE research recommendations for epidemiology
Bibliographic record
Abstract
This review aimed at determining the prevalence and incidence of Myalgic ncephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in Europe. We conducted a primary search in Scopus, PubMed and Web of Science for publications between 1994 and 15 June 2019 (PROSPERO: CRD42017078688). Additionally, we performed a backward-(reference lists) and forward-(citations) search of the works included in this review. Grey literature was addressed by contacting all members of the European Network on ME/CFS (EUROMENE). Independent reviewers searched, screened and selected studies, extracted data and evaluated the methodological and reporting quality. For prevalence, two studies in adults and one study in adolescents were included. Prevalence ranged from 0.1% to 2.2%. Two studies also included incidence estimates. In conclusion, studies on the prevalence and incidence of ME/CFS in Europe were scarce. Our findings point to the pressing need for well-designed and statistically powered epidemiological studies. To overcome the shortcomings of the current state-of-the-art, EUROMENE recommends that future research is better conducted in the community, reviewing the clinical history of potential cases, obtaining additional objective information (when needed) and using adequate ME/CFS case definitions; namely, the Centers for Disease Control & Prevention−1994, Canadian Consensus Criteria, or Institute of Medicine criteria.
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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 | 0.031 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.028 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".