Inspiratory muscle training improves autonomic function in myalgic encephalomyelitis/chronic fatigue syndrome and post-acute sequelae of SARS-CoV-2: A pilot study
Bibliographic record
Abstract
Post-acute sequelae of SARS-CoV-2 (PASC), or Long COVID, and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are debilitating post-viral conditions with many symptomatic overlaps, including exercise intolerance and autonomic dysfunction. Both conditions are growing in prevalence, and effective safe treatment strategies must be investigated. We hypothesized that inspiratory muscle training (IMT) could be used in PASC and mild to moderate ME/CFS to mitigate symptoms, improve exercise capacity, and improve autonomic function. We recruited healthy controls (n=12; 10 women), people with PASC (n=9; 8 women), and people with mild to moderate ME/CFS (n=12; 10 women) to complete 8 weeks of IMT. This project was registered as a clinical trial (NCT05196529) with clinicaltrials.gov. After completion of IMT, all groups experienced improvements in inspiratory muscle pressure (p<0.001), 6-minute walk distance (p=0.002), resting heart rate (p=0.037), heart rate variability (p<0.05), and symptoms related to sleep (p=0.009). In the ME/CFS group only, after completion of IMT, there were additional improvements with regard to vascular function (p=0.001), secretomotor function (p=0.023), the total weighted score (p=0.005) of the COMPASS 31 autonomic questionnaire, and symptoms related to pain (p=0.016). We found that after 8 weeks of IMT, people with PASC and/or ME/CFS could see some overall improvements in their autonomic function and symptomology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".