Lung ultrasound predicts non-invasive ventilation outcome in COVID-19 acute respiratory failure: a pilot study
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to determine relationships between lung aeration assessed by lung ultrasound (LUS) with non-invasive ventilation (NIMV) outcome, intensive care unit (ICU) admission and mechanical ventilation (MV) needs in COVID-19 respiratory failure. METHODS: A cohort of adult patients with COVID-19 respiratory failure underwent LUS during initial assessment. A simplified LUS protocol consisting in scanning six areas, three for each side, was adopted. A score from 0 to 3 was assigned to each area. Comprehensive LUS score (LUSsc) was calculated as the sum of the score in all areas. LUSsc, the amount of involved sonographic lung areas (LUSq), the number of lung quadrants radiographically infiltrated and the degree of oxygenation impairment at admission (SpO<inf>2</inf>/FiO2 ratio) were compared to NIMV Outcome, MV needs and ICU admission. RESULTS: Among 85 patients prospectively included in the analysis, 49 of 61 needed MV. LUSsc and LUSq were higher in patients who required MV (median 12 [IQR 8-14] and median 6 [IQR 4-6], respectively) than in those who did not (6 [IQR 2-9] and 3 [IQR 1-5], respectively), both P<0.001. NIMV trial failed in 26 patients out 36. LUSsc and LUSq were significantly higher in patients who failed NIMV than in those who did not. From ROC analysis, LUSsc ≥12 and LUSq ≥5 gave the best cut-off values for NIMV failure prediction (AUC=0.95, 95%CI 0.83-0.99 and AUC=0.81, 95% CI 0.65-0.91, respectively). CONCLUSIONS: Our data suggest LUS as a possible tool for identifying patients who are likely to require MV and ICU admission or to fail a NIMV trial.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".