Accuracy of Medical Students in Detecting Pleural Effusion Using Lung Ultrasound as an Adjunct to the Physical Examination
Bibliographic record
Abstract
OBJECTIVES: This study compared the accuracy of medical students in identifying pleural effusion in hospitalized patients using the physical examination versus lung ultrasound (US). METHODS: Fourth-year medical students (n = 14) received 20 hours of general practical US training (including 2 hours of specialized lung US training) plus theoretical and video documentation. The students used the physical examination alone versus the physical examination plus lung US to document the presence or absence of pleural effusion in the right and left hemithoraces of hospitalized patients (n = 11 patients; 22 hemithoraces examined 544 times in total). The reference standard for identification of pleural effusion was a lung US examination by 2 expert point-of-care sonographers. RESULTS: The odds of correctly identifying the presence versus absence of pleural effusion was 5 times greater with lung US as an adjunct to the physical examination compared to the physical examination alone (odds ratio [OR], 5.1 from multivariate logistic regression; 95% confidence interval, 3.3-8.0). The addition of lung US to the physical examination resulted in an increase in sensitivity from 48% to 90%, in specificity from 73% to 86%, and in accuracy from 60% to 88%. The benefits of using US were greater when pleural effusion was present versus absent (OR, 10.8 versus 2.4) and when examining older versus younger patients (OR, 10.2 versus 2.8). CONCLUSIONS: These results demonstrate that medical students' ability to detect the presence or absence of pleural effusion is superior when using lung US as an adjunct to the physical examination than when using the physical examination alone.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".