Predictive value of bedside diaphragmatic ultrasonography for extubation success in critically ill patients after general anaesthesia: A meta-analysis with trial sequential analysis (TSA)
Bibliographic record
Abstract
Background and Aims: Accurate prediction of extubation success is crucial in critical care to avoid complications from premature or prolonged mechanical ventilation. Bedside diaphragmatic ultrasonography has emerged as a promising tool for assessing extubation readiness, but its effectiveness requires further validation. This meta-analysis evaluates the effectiveness of this method and uses trial sequential analysis (TSA) to assess evidence reliability and identify the need for further research. Methods: A comprehensive literature search was conducted across PubMed, Medline, Embase, Cochrane CENTRAL, Ovid, ISI Web of Science, and the Wanfang Database from 2014 to 2023. The included studies assessed diaphragmatic ultrasonography for predicting extubation success. Successful extubation is defined as maintaining spontaneous breathing for at least 48 hours post-extubation. Data extraction and quality assessment were performed using a random-effects model. Quality was assessed via the Newcastle-Ottawa Scale (NOS), and publication bias was evaluated through funnel plots and Egger’s test. Cumulative meta-analysis, sub-group analyses, and TSA were used to explore heterogeneity and assess evidence reliability. Results: Fourteen studies were included, demonstrating high quality. Diaphragm excursion (DE) and diaphragm thickening fraction (DTF) were significant predictors of extubation success. The diagnostic odds ratio (DOR) was 4.80 [95% confidence interval (CI): 3.86, 5.97)], with a sensitivity of 81.48% and a specificity of 86.86%. Significant heterogeneity was observed ( I 2 =85%, χ 2 =87.19, P < 0.00001). TSA indicated that the cumulative evidence was insufficient. Conclusions: Diaphragmatic ultrasound, particularly DE and DTF, is useful for predicting extubation success, but current evidence is inconclusive. Further research is required to confirm these findings.
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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 | 0.034 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.068 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".