Diaphragmatic ultrasonography as a predictor for mechanical ventilation weaning in adults: an integrative review
Bibliographic record
Abstract
ABSTRACT Diaphragmatic ultrasonography (US) has been used to evaluate several respiratory diseases. Due to portability, this examination can easily be performed at bedside, in wards, emergency rooms and especially in the intensive care unit To identify and analyze how diaphragmatic ultrasonography can support safe mechanical ventilation weaning based on scientific evidence. This is an integrative literature review. Bibliographic survey was conducted from September to November 2022 in the following electronic databases: SciELO, LILACS and PubMed. A total of eight articles were included and evaluated using the Newcastle-Ottawa and PEDro scales, showing intermediate to high methodological quality. In total, the studies included 482 adult volunteers, of both sexes, aged 58.09±8.03, who had undergone invasive mechanical ventilation for at least 24 hours and were under ventilation to continue invasive ventilation weaning. In the selected studies, we found three evaluative variables: diaphragmatic excursion (DE) (75%); diaphragmatic thickness (DT) (62.5%); and diaphragmatic thickening fraction (DTF) (100%). Incorporating DT, DE and DTF into protocols for mechanical ventilation weaning in critically ill patients seems to support decision-making on the ideal time for weaning, reducing extubation failure.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".