Lung ultrasound score for the assessment of lung aeration in ARDS patients: comparison of two approaches
Bibliographic record
Abstract
Abstract Purpose A 4-step lung ultrasound (LUS) score has been previously used to quantify lung density. We compared 2 versions of this scoring system for distinguishing severe from moderate loss of aeration in ARDS: coalescence-based score (cLUS) vs. quantitative-based score (qLUS – >50% pleura occupied by artefacts). Materials and Methods We compared qLUS and cLUS to lung density measured by quantitative CT scan in 12 standard thoracic regions. A simplified approach (1 scan per region) was compared to an extensive one (regional score computed as the mean of all relevant intercostal space scores). Results We examined 13 conditions in 7 ARDS patients (7 at PEEP 5, 6 at PEEP 15 cmH2O-156 regions, 398 clips). Switching from cLUS to qLUS resulted in a change in interpretation in 117 clips (29.4%, 1-point reduction) and in 41.7% of the regions (64 decreases (range 0.2–1), 1 increase (0.2 points)). Regional qLUS showed very strong correlation with lung density (rs=0.85), higher than cLUS (rs=0.79; p=0.010). The agreement with CT classification in well aerated, poorly aerated, and not aerated tissue was moderate for cLUS (agreement 65.4%; Cohen’s K coefficient 0.475 (95%CI 0.391–0.547); p<0.0001) and substantial for qLUS (agreement 81.4%; Cohen’s K coefficient 0.701 (95%CI 0.653–0.765), p<0.0001). The agreement between single spot and extensive approaches was almost perfect (cLUS: agreement 89.1%, Cohen’s kappa coefficient 0.840 (95%CI 0.811–0.911), p<0.0001; qLUS: agreement 86.5%, Cohen’s kappa coefficient 0.819 (95%CI 0.761–0.848), p<0.0001). Conclusion A LUS score based on the percentage of occupied pleura performs better than a coalescence-based approach for quantifying lung density. A simplified approach performs as well as an extensive one.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".