Accuracy and inter‐rater reliability of lung auscultation by bovine practitioners when compared with ultrasonographic findings
Bibliographic record
Abstract
In practice, veterinary surgeons frequently rely on lung auscultation as a confirmation test for pneumonia. To what extent diagnostic accuracy of lung auscultation varies between different practitioners is currently unknown. In this diagnostic test study, 49 Dutch veterinarians each auscultated between 8 and 10 calves, and communicated whether they would decide to treat the animal with antimicrobials or not. They were not allowed to perform any other aspect of the clinical examination. Their decisions were compared with lung ultrasonography findings. The average sensitivity and specificity of lung auscultation were 0.63 (sd=0.2; range=0.2-1.0) and 0.46 (sd=0.3; range=0.0-1.0), respectively. Of the participants, 8.2 per cent were 100 per cent sensitive, 16.3 per cent were 100 per cent specific, and only 4.0 per cent were perfect. The Krippendorff's alpha was 0.18 (95 per cent confidence interval: -0.01 to 0.38), signifying poor reliability between multiple raters. Regardless of the poor diagnostic accuracy in this study, especially the large variation in a confirmation test between different practitioners could potentially cause professional damage as well as misuse of antimicrobials. This study could be seen as a gentle stimulus to regularly evaluate one's diagnostic skills. Both complementary training and the use of more accurate techniques with less inter-rater variation could improve the situation.
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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 | 0.057 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".