Post-SARS-CoV-2 Onset Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Symptoms in Two Cohort Studies of COVID-19 Recovery
Bibliographic record
Abstract
Objective: To determine how many people with long COVID also meet diagnostic criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Methods: We identified which participants with long COVID also met the Institute of Medicine (IOM) or the 2003 Canadian Consensus Criteria (CCC) for ME/CFS at approximately 6-8 months post-SARS-CoV-2 infection in two cohorts: (1) the JHU COVID Recovery cohort, which enrolled participants within 4 weeks of infection and (2) the Long-term Impact of Infection with Novel Coronavirus (LIINC) cohort, which enriched for participants with long COVID. Neither study administered ME/CFS-specific surveys, so available data elements were mapped onto each ME/CFS diagnostic criteria. Results: Of 97 JHU participants with long COVID, 5 met IOM criteria and 2 met CCC criteria. Of 281 LIINC participants with long COVID, 51 met the IOM criteria and 29 met the CCC criteria. In LIINC, participants with long COVID meeting ME/CFS criteria were more likely to be female and report a greater number of post-COVID symptoms (p<0.001). Conclusions: The co-occurrence of ME/CFS symptoms and long COVID suggests that SARS-CoV-2 is a cause of ME/CFS. ME/CFS-specific measures should be incorporated into studies of post-acute COVID-19 to advance studies of post-SARS-CoV-2 onset ME/CFS.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".