Expert Agreement in the Interpretation of Lung Ultrasound Studies Performed on Mechanically Ventilated Patients
Bibliographic record
Abstract
OBJECTIVES: Although lung ultrasound (US) has been shown to have high diagnostic accuracy in patients presenting with acute dyspnea, its precision in critically ill patients is unknown. We investigated common areas of agreement and disagreement by studying 6 experts as they interpreted lung US studies in a cohort of intensive care unit (ICU) patients. METHODS: A previous study by our group asked experts to rate the quality of 150 lung US studies performed by 10 novices in a population of mechanically ventilated patients. For this study, experts were asked to interpret them without the clinical context, reporting the presence of pneumothorax, interstitial syndrome, consolidation, atelectasis, or pleural effusion. RESULTS: The rate of expert agreement depended on how it was defined, ranging from 51% (with a strict definition of agreement) to 57% (with a more liberal definition). Removing cases involving lung consolidation (the most common source of disagreement) improved the rates of agreement to 69% and 86%, respectively. CONCLUSIONS: The frequency of agreement was lower than might have been expected in this study. Several potential reasons are identified, chief among them the fact that ICU patients often develop multiple pulmonary insults, making agreement on a specific primary diagnosis challenging. This finding suggests that the utility of lung US in identifying the main contributing lung condition in ICU patients may be lower than in dyspneic patients encountered in the emergency department. It also raises the possibility that the clinical context is more important for lung US than other imaging modalities.
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How this classification was reachedexpand
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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".