Thoracic Ultrasonography and Bronchoalveolar Lavage Fluid Analysis in Holstein Calves with Subclinical Lung Lesions
Bibliographic record
Abstract
BACKGROUND: Thoracic ultrasonography (US) and bronchoalveolar lavage fluid (BALF) analysis are antemortem methods used to identify the lung lesions associated with bovine respiratory disease (BRD). Accuracy of US and the cell distributions in BALF have not been characterized in calves with subclinical disease. OBJECTIVES: To evaluate the accuracy of US and BALF and describe BALF characteristics in calves with subclinical lung lesions. ANIMALS: Twenty-five Holstein calves, 1-12 weeks old. METHODS: In this prospective study, calves with low respiratory scores underwent US, BALF and postmortem examination (normal US, n = 5; comet-tails, n = 5; consolidation, n = 15). Bronchoalveolar lavage fluid was collected and analyzed for total and differential cell counts. Lung lesions were assessed by gross and histopathologic examination. Data were analyzed using nonparametric methods and relative risk analysis. The accuracy of US and BALF were estimated relative to postmortem examination. RESULTS: The sensitivity and specificity of US for detecting lung lesions was 94% (95% CI, 69-100%) and 100% (95% CI, 64-100%), respectively. A cut-point of ≥4% BALF neutrophils was associated with the highest BALF sensitivity and specificity, 81% (95% CI, 56-94%) and 75% (95% CI, 36-95%). The presence of consolidation on US increased the risk of having a BALF neutrophil proportion ≥4% (RR, 3.9; 95% CI, 1.13-13.45; P = .003). CONCLUSIONS AND CLINICAL IMPORTANCE: Ultrasonography accurately detects lung lesions in calves with subclinical disease. Clinicians should use a cut-point of ≥4% BALF neutrophils to diagnose subclinical respiratory disease.
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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 | 0.004 | 0.008 |
| 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 | 0.001 |
| 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".