Development and validation of a clinical respiratory disease scoring system for guiding treatment decisions in veal calves using a Bayesian framework
Bibliographic record
Abstract
Active infectious bovine respiratory disease (BRD) is an infection of the airways that needs to be diagnosed correctly so that appropriate treatment can be initiated. The simplest and most practical test to detect active BRD in dairy calves raised for veal is the detection and interpretation of clinical signs by producers or technicians. However, the clinical scoring system currently available for veal calves lacks sensitivity and specificity, contributing to economic losses and high use of antimicrobials. An accurate and reliable batch-level test to detect active BRD is essential to tailor antimicrobial use and reduce economic losses in veal calves. The objective of this study was therefore to develop and validate a new veal calf respiratory clinical scoring system (VcCRS), including reliable clinical signs (cough, ear droop or head tilt) and increased rectal temperature to detect active BRD in batches of veal calves housed individually, and to describe the accuracy of the scoring system for identifying batches of veal calves to treat. During 2017 to 2018, clinical examination, thoracic ultrasonography (TUS) and a haptoglobin concentration (Hap) were prospectively performed on 800 veal calves housed individually in Québec, Canada. Deep nasopharyngeal swabs were performed on 250 veal calves. A Bayesian latent class model accounting for imperfect accuracy of TUS and Hap was used to obtain weights for the clinical signs and develop the VcCRS. The VcCRS was then validated externally in 3 separate data sets. Finally, the applicability of the VcCRS at batch level was determined. We found that calves with 2 of the following findings-cough, unilateral or bilateral ear droop or head tilt, or increased rectal temperature ≥39.7°C-were considered positive and had a 31% chance of having active BRD. Without at least 2 of these 2 findings, a calf had a 100% chance of not having active BRD. At the batch level, we found that a batch with ≥3 positive calves among 10 calves sampled 2 wk after arrival at the fattening unit had a 94% chance of having an active BRD prevalence ≥10%. A batch with <3 positive calves had a 95% chance of not having an active BRD prevalence ≥10%. In this study, we developed a simple individual and batch-level score that is reliable across examiners and performs effectively in the detection of active BRD in veal calves. The implementation of this VcCRS in the veal calf industry would promote the elaboration of a protocol tailoring antimicrobial use.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".