Respiratory muscle dysfunction in acute and chronic respiratory failure: how to diagnose and how to treat?
Bibliographic record
Abstract
Assessing and treating respiratory muscle dysfunction is crucial for patients with both acute and chronic respiratory failure. Respiratory muscle dysfunction can contribute to the onset of respiratory failure and may also worsen due to interventions aimed at treatment. Evaluating respiratory muscle function is particularly valuable for diagnosing, phenotyping and assessing treatment efficacy in these patients. This review outlines established methods, such as measuring respiratory pressures, and explores novel techniques, including respiratory muscle neurophysiology assessments using electromyography and imaging with ultrasound.Additionally, we review various treatment strategies designed to support and alleviate the burden on overworked respiratory muscles or to enhance their capacity through training interventions. These strategies range from invasive and noninvasive mechanical ventilation approaches to specialised respiratory muscle training programmes. By summarising both established techniques and recent methodological advancements, this review aims to provide a comprehensive overview of the tools available in clinical practice for evaluating and treating respiratory muscle dysfunction. Our goal is to present a clear understanding of the current capabilities and limitations of these diagnostic and therapeutic approaches. Integrating advanced diagnostic methods and innovative treatment strategies should help improve patient management and outcomes. This comprehensive review serves as a resource for clinicians, equipping them with the necessary knowledge to effectively diagnose and treat respiratory muscle dysfunction in both acute and chronic respiratory failure scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".