A pilot cross-sectional investigation of symptom clusters and associations with patient-reported outcomes in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Post COVID-19 Condition
Bibliographic record
Abstract
BACKGROUND: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is associated with long-term disability and poor quality of life (QoL). Cardinal ME/CFS symptoms (including post-exertional malaise, cognitive dysfunction and sleep disturbances) have been observed in Post COVID-19 Condition (PCC). To gain further insight into the potential role of ME/CFS as a post-COVID-19 sequela, this study investigates associations between symptoms and patient-reported outcomes, as well as symptom clusters. METHODS: Participants included Australian residents aged between 18 and 65 years formally diagnosed with ME/CFS fulfilling the Canadian or International Consensus Criteria or PCC meeting the World Health Organization case definition. Validated, self-administered questionnaires collected participants' sociodemographic and illness characteristics, symptoms, QoL and functional capacity. Associations between symptoms and patient-reported outcomes were investigated with multivariate linear regression models. Hierarchical cluster analysis was performed to identify symptom clusters. RESULTS: Most people with ME/CFS (pwME/CFS) and people with PCC (pwPCC) were female (n = 48/60, 80.0% and n = 19/30, 63.3%, respectively; p = 0.12). PwME/CFS were significantly younger (x̄=41.75, s = 12.91 years) than pwPCC (x̄=48.13, s =10.05 years; p =0.017). Autonomic symptoms (notably dyspnoea) were associated with poorer scores in most patient-reported outcome domains for both cohorts. None of the four symptom clusters identified were unique to ME/CFS or PCC. Clusters were largely delineated by the presence of gastrointestinal and neurosensory symptoms, illness duration, ME/CFS criteria met and total symptoms. CONCLUSIONS: Illness duration may explain differences in symptom burden between pwME/CFS and pwPCC. PCC diagnostic criteria must be refined to distinguish pwPCC at risk of long-term ME/CFS-like illness and subsequently deliver necessary care and support.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".