Electrophysiological neuromuscular alterations and severe fatigue predict long-term muscle weakness in survivors of COVID-19 acute respiratory distress syndrome
Bibliographic record
Abstract
Introduction: Long-term weakness is common in survivors of COVID-19-associated acute respiratory distress syndrome (CARDS). We longitudinally assessed the predictors of muscle weakness in patients evaluated 6 and 12 months after intensive care unit discharge with in-person visits. Methods: Muscle strength was measured by isometric maximal voluntary contraction (MVC) of the tibialis anterior muscle. Candidate predictors of muscle weakness were follow-up time, sex, age, mechanical ventilation duration, use of steroids in the intensive care unit, the compound muscle action potential of the tibialis anterior muscle (CMAP-TA-S100), a 6-min walk test, severe fatigue, depression and anxiety, post-traumatic stress disorder, cognitive assessment, and body mass index. We also compared the clinical tools currently available for the evaluation of muscle strength (handgrip strength and Medical Research Council sum score) and electrical neuromuscular function (simplified peroneal nerve test [PENT]) with more objective and robust measures of force (MVC) and electrophysiological evaluation of the neuromuscular function of the tibialis anterior muscle (CMAP-TA-S100) for their essential role in ankle control. Results: = 0.036) were independent predictors of MVC. MVC was strongly associated with handgrip strength, whereas CMAP-TA-S100 was strongly associated with PENT. Discussion: Electrical neuromuscular abnormalities and severe fatigue are independently associated with reduced MVC and can be used to predict the risk of long-term muscle weakness in CARDS survivors.
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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 | 0.001 |
| 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".