Fatigue presentation, severity, and related outcomes in a prospective cohort following post-COVID-19 hospitalization in British Columbia, Canada
Bibliographic record
Abstract
Introduction: Increasing evidence on long-term health outcomes following SARS CoV-2 infection shows post-viral symptoms can persist for months. These symptoms are often consistent with those of Myalgic Encephalomyelitis or Chronic Fatigue Syndrome (ME/CFS). The aim of the present study was to examine the prevalence and outcome predictors of post-viral fatigue and related symptoms 3- and 6-months following symptom onset. Methods: = 88) were recruited from a Post-COVID-19 Respiratory Clinic (PCRC) in Vancouver, Canada to examine predictors of long-term fatigue and substantial fatigue. Multivariable mixed effects analyses examined the relationship between patient predictors, including pre-existing comorbidities, patient reported outcome measures, and fatigue and substantial fatigue at follow-up. Results: The number of patients experiencing fatigue or substantial fatigue at 3 months post-infection were 58 (67%) and 14 (16%) respectively. At 6 months these numbers declined to 47 (60%) patients experiencing fatigue and 6 (6%) experiencing substantial fatigue. Adjusted analysis, for sex, age, and time, revealed the number of pre-existing comorbidities to be associated with fatigue (OR 2.21; 95% CI 1.09-4.49; 0.028) and substantial fatigue (OR 1.73; 95% CI 1.06-2.95; 0.033) at 3 months follow-up. Except for shortness of breath, self-care, and follow-up time, all follow-up variables were found to be associated with fatigue and substantial fatigue at 3 months. Conclusion: Fatigue and substantial fatigue are common after COVID-19 infection but often diminish over time. A significant number of patients continue to exhibit long-term fatigue at 6 months follow-up. Further research is needed to clarify the causality of viral infections in the development and severity of fatigue as a symptom and in meeting post-viral fatigue syndrome or ME/CFS diagnostic criteria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".