Lung ultrasound in heart failure: Lessons from re‐analysis of Lung Ultrasound 2011 database
Bibliographic record
Abstract
Abstract Introduction: In the setting of patients presenting with shortness of breath to an Emergency Department a simple lung ultrasound protocol aimed at detecting pulmonary oedema has been shown to have diagnostic accuracy of 85%. This article reviews data from the original study, in an attempt to determine whether adjusting the protocol and/or interpretive criteria would improve results. Method: A large lung ultrasound project provided the dataset. Inter‐rater and intertest discrepancies were reviewed. Then original stored images and comments were retrospectively analysed using alternate interpretive criteria. Specific variations included changing the number of B‐lines required to define ‘wet lung’ and assessing other pleural line abnormalities. Where they had been acquired cardiac loops were reviewed in addition to the lung images. Results: The 204 original studies available were reviewed. Some disagreement could be attributed to inexperience and unclear definitions. Adjusting the number of B‐lines did not improve diagnostic accuracy. All positive scans, with numerous B‐lines were reviewed using more advanced diagnostic criteria (pleural line abnormalities) and the number of false positives was decreased. In cases where cardiac views were available, their inclusion was beneficial. Conclusion: A simple lung ultrasound protocol to assess for ‘wet lung’ in patients presenting to Emergency Departments provides diagnostic accuracy of around 85% in the hands of relative novices. More advanced interpretation of the same_ultrasound images, and the addition of cardiac views, is likely to further improve diagnostic accuracy.
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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 | 0.052 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".