Clinical Characteristics of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) Diagnosed in Patients with Long COVID
Bibliographic record
Abstract
Background and Objectives: COVID-19 can be serious not only in the acute phase but also after the acute phase and some patients develop ME/CFS. There have been few studies on patients with long COVID in whom ME/CFS was diagnosed by physicians based on standardized criteria after examinations and exclusion diagnosis and not based on only subjective symptoms. The purpose of this study was to elucidate the detailed characteristics of ME/CFS in patients with long COVID. Materials and Methods: A retrospective descriptive study was performed for patients who visited a COVID-19 aftercare clinic established in Okayama University Hospital during the period was from February 2021 to April 2022. Results: Clinical data were obtained from medical records for 281 patients, and 279 patients who met the definition of long COVID were included. The overall prevalence rate of ME/CFS diagnosed by three sets of ME/CFS criteria (Fukuda, Canadian and IOM criteria) was 16.8% (48.9% in male and 51.1% in females). The most frequent symptoms in ME/CFS patients were general fatigue and post-exertional malaise (89.4% of the patients), headache (34.0%), insomnia (23.4%), dysosmia (21.3%) and dysgeusia (19.1%). Dizziness, chest pain, insomnia and headache were characteristic symptoms related to ME/CFS. The male to female ratio in ME/CFS patients was equal in the present study, although ME/CFS was generally more common in women in previous studies. Given that patients with ME/CFS had more severe conditions in the acute phase of COVID-19, the severity of the acute infectious state might be involved in the pathophysiology of ME/CFS. Conclusions: The prevalence rate of ME/CFS and the characteristic sequelae in the long COVID condition were revealed in this study.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".