Lung ultrasound score for the assessment of lung aeration in ARDS patients: comparison of two approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it