Fatigue following COVID-19 infection is not associated with autonomic dysfunction
Bibliographic record
Abstract
BACKGROUND: The long-term clinical and physiological consequences of COVID-19 infection remain unclear. While fatigue has emerged as a common symptom following infection, little is known about its links with autonomic dysfunction. SARS-CoV-2 is known to infect endothelial cells in acute infection, resulting in autonomic dysfunction. Here we set out to test the hypothesis that this results in persistent autonomic dysfunction and is associated with post-COVID fatigue in convalescent patients. METHODS: We recruited 20 fatigued and 20 non-fatigued post-COVID patients (median age 44.5 years, 36/40 (90%) female, median time to follow up 166.5 days). Fatigue was assessed using the Chalder Fatigue Scale. These underwent the Ewing's autonomic function test battery, including deep breathing, active standing, Valsalva manoeuvre and cold-pressor testing, with continuous electrocardiogram and blood pressure monitoring, as well as near-infrared spectroscopy-based cerebral oxygenation. 24-hour ambulatory blood pressure monitoring was also conducted, and patients completed the generalised anxiety disorder-7 questionnaire. We assessed between-group differences in autonomic function test results and used unadjusted and adjusted linear regression to investigate the relationship between fatigue, anxiety, and autonomic test results. RESULTS: We found no pathological differences between fatigued and non-fatigued patients on autonomic testing or on 24-hour blood pressure monitoring. Symptoms of orthostatic intolerance were reported by 70% of the fatigued cohort at the time of active standing, with no associated physiological abnormality detected. Fatigue was strongly associated with increased anxiety (p <0.001), with no patients having a pre-existing diagnosis of anxiety. CONCLUSIONS: These results demonstrate the significant burden of fatigue, symptoms of autonomic dysfunction and anxiety in the aftermath of COVID-19 infection, but reassuringly do not demonstrate pathological findings on autonomic testing.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".