Comparing accuracy of bedside ultrasound examination with physical examination for detection of pleural effusion
Bibliographic record
Abstract
BACKGROUND: In detecting pleural effusion, bedside ultrasound (US) has been shown to be more accurate than auscultation. However, US has not been previously compared to the comprehensive physical examination. This study seeks to compare the accuracy of physical examination with bedside US in detecting pleural effusion. METHODS: This study included a convenience sample of 34 medical inpatients from Calgary, Canada and Spokane, USA, with chest imaging performed within 24 h of recruitment. Imaging results served as the reference standard for pleural effusion. All patients underwent a comprehensive lung physical examination and a bedside US examination by two researchers blinded to the imaging results. RESULTS: Physical examination was less accurate than US (sensitivity of 44.0% [95% confidence interval (CI) 30.0-58.8%], specificity 88.9% (95% CI 65.3-98.6%), positive likelihood (LR) 3.96 (95% CI 1.03-15.18), negative LR 0.63 (95% CI 0.47-0.85) for physical examination; sensitivity 98% (95% CI 89.4-100%), specificity 94.4% (95% CI 72.7-99.9%), positive LR 17.6 (95% CI 2.6-118.6), negative LR 0.02 (95% CI 0.00-0.15) for US). The percentage of examinations rated with a confidence level of 4 or higher (out of 5) was higher for US (85% of the seated US examination and 94% of the supine US examination, compared to 35% of the PE, P < 0.001), and took less time to perform (P < 0.0001). CONCLUSIONS: US examination for pleural effusion was more accurate than the physical examination, conferred higher confidence, and required less time to complete.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".