Muscle fatigability and post‐acute <scp>COVID</scp> ‐19 syndrome: A case study
Bibliographic record
Abstract
The acute phase of COVID-19 has been well studied, however with increasing post-acute COVID-19 syndrome, much is unknown about its long-term effects. A common symptom in both the acute and post-acute phases has been fatigue, assessed predominantly qualitatively. Here we present a case study objectively assessing neuromuscular fatiguability in a young male (27 year, 1.85 m, 78 kg) who continues to experience COVID-19 related fatigue and cognitive dysfunction, including other symptoms, 12+ months post-infection. Prior to infection, he was part of a neuromuscular study forming the basis of our pre-COVID-19 results. The study was repeated 12 months post-COVID-19 infection. Muscle strength, endurance, torque steadiness, voluntary activation, twitch properties, electromyography, and compound muscle action potential were obtained and compared pre- and post-COVID-19. All measurements were done using a dorsiflexion dynamometer in which the participant also was asked to produce a one-minute fatiguing maximal voluntary contraction. Muscle strength, voluntary activation, and fatigability (slope of torque) showed no meaningful differences, suggesting intrinsic neuromuscular properties are not affected. However, torque steadiness was impaired three-fold in the post- compared with pre-COVID-19 test. The participant also reported a higher level of perceived exertion subjectively and a continued complaint of fatigue. These findings indicate that muscle fatiguability in post-acute COVID-19 syndrome may not be a limitation of the muscle and its activation, but a perceptual disconnect caused by cognitive impairments relating to physical efforts. This case report suggests the potential value of larger studies designed to assess these features in post-acute COVID-19 syndrome.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".