Symptom presentation and quality of life are comparable in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and post COVID-19 condition
Bibliographic record
Abstract
Background and Οbjective: Considerable overlap exists in the clinical presentation of Post COVID-19 Condition and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). The current study aimed to compare symptoms and patient-reported Quality of Life (QoL) among people with Post COVID-19 Condition and ME/CFS in Australia. Methods: QoL data was collected from n=61 ME/CFS patients, n=31 Post COVID-19 Condition patients, and n=54 Healthy Controls (HCs) via validated instruments. The ME/CFS and Post COVID-19 Condition participants also provided self-reported severity and frequency of symptoms derived from the Canadian and International Consensus Criteria for ME/CFS and the World Health Organization case definition for Post COVID-19 Condition. Study variables were compared with Chi-square, Fisher’s exact, Fisher-Freeman-Halton, Mann-Whitney U, and Kruskal-Wallis H tests using Statistical Package for the Social Sciences version 29. Symptom clusters among the two illness cohorts were identified with hierarchical cluster analysis. Results: ME/CFS was associated with a higher prevalence of short-term memory loss (p=0.039), muscle weakness (p<0.001), lymphadenopathy (p=0.013), and nausea (p=0.003). People with ME/CFS also reported more severe light-headedness (p=0.011) and more frequent unrefreshed sleep (p=0.011), but less frequent heart palpitations (p=0.040). Symptom prevalence, severity, and frequency were otherwise comparable. Few differences existed in the QoL of the two illness cohorts, both of which returned significantly impaired QoL scores when compared with HCs (p<0.001). Cluster analysis of symptom prevalence revealed four clusters: 1) Low gastrointestinal, low neurosensory; 2) Moderate gastrointestinal, low orthostatic and memory loss; 3) Moderate gastrointestinal, high orthostatic and memory loss; and 4) High gastrointestinal, high pain, which did not differ in sociodemographic information, illness status, or diagnostic criteria met. Conclusions: Post COVID-19 Condition and ME/CFS are remarkably similar in presentation and, like ME/CFS, Post COVID-19 Condition has a profound and negative impact on patient QoL. Gastrointestinal symptoms may have a role in determining ME/CFS and Post COVID-19 Condition subtypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".