Diagnostic accuracy of basic lung ultrasound in breathless patients over 60 years of age; stressing the protocol
Bibliographic record
Abstract
Abstract Introduction: Emergency department differentiation of pulmonary oedema from chronic obstructive airways disease causing acute breathlessness is inaccurate 25% of the time despite clinical acumen, clinician‐reported chest x‐ray and ECG. This research investigates whether a basic lung ultrasound protocol (LUS) could improve identification of pulmonary oedema in breathless elderly patients. Method: Researchers prospectively sampled patients over 60 years, describing any breathlessness on presentation to a suburban emergency department. LUS studies were acquired by experienced or novice sonologists, interpreted by a blinded reviewer and compared with cardiologist chart audit for diagnosis at admission (gold standard). The admitting doctor's diagnosis, blinded to LUS, was compared with the chart audit result. Results: 204 LUS were collected, 145 by experienced sonologist and 59 by inexperienced. Diagnostic accuracy compared to cardiologist audit was 86.2% (95% CI 80.9 to 90.3), significantly higher than 70.2%, diagnostic accuracy for admission diagnosis, difference in proportion of 16% (95%CI 7.7 to 24.4%). Conclusion: A simple lung scanning protocol can help exclude pulmonary oedema in any breathless elderly patient.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".