Thoracic Ultrasonography Findings and Their Association With Respiratory Pathogens in 221 Young Beef Cattle at Fattening Farms: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Thoracic ultrasonography (TUS) could improve antibiotic treatment selection in cattle with respiratory diseases. HYPOTHESIS/OBJECTIVES: Evaluate the association between respiratory pathogens and consolidations on TUS in feedlot cattle, at both individual and group levels. ANIMALS: A total of 221 bulls, aged 8.8 months and weighing 322.5 ± 160 kg, from nine farms. METHODS: Cross-sectional study including all data from clinical examinations and TUS collected weekly during the first month on feed. Pathogens were assessed by seroconversion (all animals) and qPCR on nasal swabs (sick animals). At the individual level, the association between pathogen detection and TUS consolidation was investigated using univariate logistic regression, and the ability of consolidation size to differentiate bacterial from non-bacterial pneumonia was assessed using receiver operating characteristic curves. Principal component analysis identified clusters at the group level based on pathogen detection and TUS results. RESULTS: in the scanned thoracic region differentiated bacterial from non-bacterial pneumonia with a sensitivity of 47.8% (95% CI, 36.4-83.3) and specificity of 94.1% (95% CI, 60.0-100.0). These results were consistent at the group level; clustering based on bacterial versus non-bacterial etiology correlated with the number and size of consolidations. CONCLUSIONS AND CLINICAL IMPORTANCE: Consolidation size could help differentiate bacterial from non-bacterial pneumonia, guiding treatment at both individual and group levels.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".