COVID-19 and post-infectious myalgic encephalomyelitis/chronic fatigue syndrome: a narrative review
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a viral infection which can cause a variety of respiratory, gastrointestinal, and vascular symptoms. The acute illness phase generally lasts no more than 2-3 weeks. However, there is increasing evidence that a proportion of COVID-19 patients experience a prolonged convalescence and continue to have symptoms lasting several months after the initial infection. A variety of chronic symptoms have been reported including fatigue, dyspnea, myalgia, exercise intolerance, sleep disturbances, difficulty concentrating, anxiety, fever, headache, malaise, and vertigo. These symptoms are similar to those seen in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), a chronic multi-system illness characterized by profound fatigue, sleep disturbances, neurocognitive changes, orthostatic intolerance, and post-exertional malaise. ME/CFS symptoms are exacerbated by exercise or stress and occur in the absence of any significant clinical or laboratory findings. The pathology of ME/CFS is not known: it is thought to be multifactorial, resulting from the dysregulation of multiple systems in response to a particular trigger. Although not exclusively considered a post-infectious entity, ME/CFS has been associated with several infectious agents including Epstein-Barr Virus, Q fever, influenza, and other coronaviruses. There are important similarities between post-acute COVID-19 symptoms and ME/CFS. However, there is currently insufficient evidence to establish COVID-19 as an infectious trigger for ME/CFS. Further research is required to determine the natural history of this condition, as well as to define risk factors, prevalence, and possible interventional strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".