Agreement of Specific Lung Sounds Auscultation by Veterinarians for the Detection of Bronchopneumonia in Calves
Bibliographic record
Abstract
Abstract Background Lung auscultation is a common method for the routine diagnosis of calf bronchopneumonia. However, its repeatability among operators has been criticized. Objective Determine agreement among veterinarians for specific lung sounds after a short tutorial to standardize the definition of lung sounds. Animals Forty lung sounds from a larger dataset collected at 4 veal calf farms that housed 495–815 animals were submitted online to 10 different veterinarians. Methods After a short tutorial on lung sound auscultation, the raters were asked to detect the presence of any abnormal sounds and to differentiate among wheezes, crackles, and bronchial sounds. Raw percentage of agreement (PA), Gwet's agreement coefficient type 1 (AC1), Krippendorff's alpha (Ka), and Fleiss kappa (KFleiss) were chosen as agreement indicators in the absence of a gold standard indicator to assess agreement. The different indicators were interpreted based on a priori reported benchmarks. Results The agreements were fair to good for almost all lung sound indicators. For the presence of any abnormal lung sound, the reported agreements (95% confidence intervals [CI]) were 0.781 (0.716–0.845), 0.646 (0.514–0.777), 0.403 (0.351–0.455), and 0.293 (0.137–0.493) for PA, AC1, Ka, and KFleiss, respectively. The same indicators were 0.769 (0.694–0.845), 0.615 (0.446–0.784), 0.426 (0.378–0.475), and 0.425 (0.293–0.563) for wheezes, 0.754 (0.685–0.823), 0.643 (0.503–0.782), 0.21 (0.146–0.275), and 0.208 (0.097–0.327) for crackles, and 0.636 (0.571–0.701), 0.345 (0.179–0.512), 0.182 (0.131–0.232), and 0.18 (0.081–0.279) for bronchial sound detections, respectively. Conclusion and Clinical Importance Agreement among raters auscultating calf respiratory sounds was higher than previously reported. However, improvement is still possible to increase auscultation agreement.
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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 | 0.037 | 0.063 |
| 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 | 0.002 |
| 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".