How is ultrasound imaging being used to assess respiratory musculature? A systematic review
Bibliographic record
Abstract
Objectives: 1) To identify studies which have used ultrasound imaging to evaluate respiratory musculature; and 2) evaluate, synthesize and compare the methodology for image acquisition and analysis. Methods: Data Sources: Five electronic databases (Medline, CINAHL, EMBASE, Scopus and Cochrane Library) with the last search run in February 2020.Eligibility: English language quantitative studies assessing ultrasound imaging of the respiratory musculature. No restriction based on clinical pathology. Data Extraction and Synthesis: Two authors independently screened studies for inclusion and performed data extraction. Results: A total of 123 papers on 8,131 participants were identified with 46% published since 2018. The three most common clinical populations were: ICU (n=48/123, 39%); respiratory (n=20/123, 16%) and neurological (n=20/123, 16%). Studies were predominantly undertaken in Europe (n=45/123, 37%) and Asia (n=31/123, 25%). Within the ICU field the main focus was on the predictive validity of diaphragm ultrasonography for successful extubation and/or weaning failure. Two studies specifically evaluated the intercostal muscle. The most commonly evaluated diaphragm muscle characteristics were: thickness, thickening fraction and excursion. There was wide variation in the ultrasound machine and probe used. If patient position was specified the most common position was supine to semi recumbent with most common imaging view being either: intercostal and/or subcostal. Conclusion: This systematic review highlights the recent literature growth on respiratory muscle imaging. There is an urgent need to understand the clinimetric properties and utility of ultrasonography as a predictive tool.
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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 | 0.028 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".