Comparison of Thoracic Auscultation, Clinical Score, and Ultrasonography as Indicators of Bovine Respiratory Disease in Preweaned Dairy Calves
Bibliographic record
Abstract
BACKGROUND: The diagnostic tools for bovine respiratory disease diagnosis include clinical inspection, thoracic auscultation, and ultrasonography. HYPOTHESIS: Thoracic auscultation and clinical examination have limitations in the detection of lung consolidation in dairy calves. ANIMALS: Prospective cohort of 106 preweaned calves from 13 different dairy herds (10 with a history of active bovine respiratory disease (BRD) in calves and 3 without suspected BRD problems). METHODS: Each preweaned calf was clinically inspected using the Wisconsin calf respiratory scoring chart (CRSC) and treatment history was noted. Systematic thoracic auscultation and ultrasonography then were performed, the latter focusing on lung consolidation. Mortality was recorded over a 30-day period. RESULTS: A total of 56 of 106 calves had ultrasonographic evidence of lung consolidation. The sensitivity of thoracic auscultation to detect consolidation was 5.9% (range, 0-16.7%). Only 41.1% (23/33) of calves with consolidated lungs had been treated previously by the producers. When adding CRSC and previous BRD treatment by the producer, sensitivity of detection increased to 71.4% (40/56). The area under the receiver operating characteristics curve was 0.809 (95% CI, 0.721-0.879) for the number of areas within the lungs with consolidation and 0.743 (95% CI, 0.648-0.823) for the maximal depth of consolidation as predictors of death within 1 month after examination. These were not significantly different (P = .06). CONCLUSIONS AND CLINICAL IMPORTANCE: This study shows that thoracic auscultation is of limited value in diagnosing lung consolidation in calves. Ultrasonographic assessment of the thorax could be a useful tool to assess BRD detection efficiency on dairy farms.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".