Diaphragm ultrasound for muscle strength assessment: A systematic literature review
Bibliographic record
Abstract
OBJECTIVE: To assess if diaphragmatic ultrasound (DU) reflects diaphragmatic muscle strenght when compared to respiratory tests and neurophysiological studies. METHODS: A systematic literature review was conducted on adults undergoing DU, compared to any respiratory or neurophysiological technique. The search strategy was applied in PubMed, Scopus, and Web of Science, and the analysis was conducted using the PRISMA methodology. Three eligibility assessment stages were performed: title, abstract, and full-text reading. The risk of bias was evaluated using the RoB 2.0, ROBINS-I, and Newcastle-Ottawa Scale tools. RESULTS: Out of 155 identified articles, 25 were selected for full-text review (14 non-randomised studies, 8 case-control studies, and 3 randomised studies). The overall risk of bias was moderate, with the main biases related to population selection and intervention assessment.Twenty-three articles used maximal inspiratory pressure measurement as a comparator which showed a weak-to-moderate correlation, significant in 10 studies, with diaphragmatic excursion. Three studies reported a weak association between diaphragmatic thickening and sniff pressure.Five articles reported a concordant correlation between diaphragmatic thickening and compound muscle action potential amplitude, significant only in one study. CONCLUSION: The variability of results obtained across different pathologies does not support the use of DU alone to predict diaphragmatic muscle strength.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".