ME/CFS: Current Insights and Future Directions
Bibliographic record
Abstract
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a chronic multisystem illness marked by persistent, disabling fatigue lasting over six months and involving various organ systems.Although recognized as a neurological disorder by the WHO since 1969, its exact cause remains unknown, and no definitive biomarkers are available.ME/CFS mainly affects adults aged 30-50, especially women, often triggered by infections like Epstein-Barr virus or SARS-CoV-2, with incidence rising after the COVID-19 pandemic.Diagnosis relies on clinical criteria and exclusion of other diseases, with post-exertional malaise (PEM), a delayed and severe symptom worsening after exertion, as a key feature.The Canadian Consensus Criteria, widely used in Europe, require specific symptoms to diagnose the disease.No curative treatment exists; management focuses on symptom relief and careful pacing to prevent PEM, alongside supportive care such as sleep optimization, pain control, and psychosocial support.Pharmacological treatments target individual symptoms, but have limited proven efficacy.Severe cases present significant challenges.ME/ CFS imposes a heavy burden on patients and families, exacerbated by diagnostic delays and limited awareness.Current research seeks to elucidate disease mechanisms and identify biomarkers for targeted therapies.Meanwhile, multidisciplinary care and increased recognition remain crucial to reduce condition's impact.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".