Lung ultrasound score and diaphragm ultrasound in weaning from mechanical ventilation: are they different in patients with and without COVID-19?
Bibliographic record
Abstract
OBJECTIVE: To compare pre-extubation physiological characteristics and ultrasound variables between patients intubated for COVID-19 compared to a clinical population and those intubated for other reasons. METHODS: This was a secondary analysis of a prospective cohort study of patients undergoing invasive mechanical ventilation (IMV) for more than 48 h. Patients were divided into two groups: those intubated for COVID-19-induced ARDS and those intubated for other clinical reasons. Ultrasound assessment of lung and diaphragm function was performed before extubation. The results were compared between the two groups of patients. RESULTS: In comparison with the patients without COVID-19, those with the disease were younger (a median age of 58 [46-76] years vs. a median age of 75 [69-85] years; p = 0.01), had fewer comorbidities (a median Charlson Comorbidity Index of 2 [1-4] vs. a median Charlson Comorbidity Index of 5 [4-6]; p < 0.01), and were less severely ill at admission (a median APACHE II score of 9 [8-14] vs. a median APACHE II score of 18 [13-22]; p < 0.01). In addition, the median duration of IMV was longer in the COVID-19 patients (11 [9-23] days vs. 6 [3-8] days; p < 0.01). Although extubation success rates were similar between the COVID-19 and non-COVID-19 groups (22 [71%] vs. 35 [77.8%]), median lung ultrasound score differed between the two groups (23 [18-25] vs. 15 [11-18]; p < 0.01), as did median diaphragmatic excursion (2.1 [1.7-2.4] vs. 1.7 [1.2-2.0]; p < 0.01). CONCLUSIONS: Although patients with COVID-19 requiring ventilatory support are younger and have fewer comorbidities than those intubated for other clinical reasons, they experience longer hospital stays. Although lung ultrasound score can differ between patients with and without COVID-19, these differences do not significantly translate into extubation success rates. Therefore, the utility of ultrasound scores in weaning COVID-19 patients from IMV needs further study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".