Incremental Value (Bayesian Framework) of Thoracic Ultrasonography over Thoracic Auscultation for Diagnosis of Bronchopneumonia in Preweaned Dairy Calves
Bibliographic record
Abstract
BACKGROUND: Thoracic ultrasonography (TUS) is a specific and relatively sensitive method to diagnose bronchopneumonia (BP) in dairy calves. Unfortunately, as it requires specific training and equipment, veterinarians typically base their diagnosis on thoracic auscultation (AUSC), which is rapid and easy to perform. HYPOTHESIS/OBJECTIVES: We hypothesized that the use of TUS, in addition to AUSC, can significantly increase accuracy of BP diagnosis. Therefore, the objectives were to (i) determine the incremental value of TUS over AUSC for diagnosis of BP in preweaned dairy calves and (ii) assess diagnostic accuracy of AUSC. ANIMALS: Two hundred and nine dairy calves (<1 month of age) were enrolled in this cross-sectional study. METHODS: Prospective cross-sectional study. All calves from a veal calves unit were examined (independent operators) using the Wisconsin Calf Respiratory Scoring Criteria (CRSC), AUSC, and TUS. A Bayesian latent class approach was used to estimate the incremental value of AUSC over TUS (integrated discrimination improvement [IDI]) and the diagnostic accuracy of AUSC. RESULTS: Abnormal CRSC, AUSC, and TUS were recorded in 3.3, 53.1, and 23.9% of calves, respectively. AUSC was sensitive (72.9%; 95% Bayesian credible interval [BCI]: 50.1-96.4%), but not specific (53.3%; 95% BCI: 43.3-64.0%) to diagnose BP. Compared to AUSC, TUS was more specific (92.9%; 95% BCI: 86.5-97.1%), but had similar sensitivity (76.5%; 95% BCI: 60.2-88.8%). The incremental value of TUS over AUSC was high (IDI = 43.7%; 5% BCI: 22.0-63.0%) significantly improving proportions of sick and healthy calves appropriately classified. CONCLUSIONS AND CLINICAL IMPORTANCE: The use of TUS over AUSC significantly improved accuracy of BP diagnosis in dairy calves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".