Detection of Bovine Coronavirus in Healthy and Diarrheic Dairy Calves
Bibliographic record
Abstract
BACKGROUND: BCoV is identified in both healthy and diarrheic calves, complicating its assessment as a primary pathogen. OBJECTIVES: To investigate the detection rates of bovine coronavirus (BCoV) in feces of healthy and diarrheic calves and to describe the usefulness of a pancoronavirus reverse transcriptase (RT) PCR (PanCoV-RT-PCR) assay to identify BCoV in samples of diarrheic calves. ANIMALS: Two hundred and eighty-six calves <21 days. Calves with liquid or semiliquid feces, temperature >39.5°C, and inappetence were considered as cases, and those that had pasty or firm feces and normal physical examination were designated as controls. METHODS: Prospective case-control study. A specific BCoV-RT-PCR assay was used to detect BCoV in fecal samples. Association between BCoV and health status was evaluated by exact and random effect logistic regression. Fecal (n = 28) and nasal (n = 8) samples from diarrheic calves were tested for the presence of BCoV by both the PanCoV-RT-PCR and a specific BCoV-RT-PCR assays. A Kappa coefficient test was used to assess the level of agreement of both assays. RESULTS: BCoV was detected in 55% (157/286) of calves; 46% (66/143), and 64% (91/143) of healthy and diarrheic calves, respectively. Diarrheic calves had higher odds of BCoV presence than healthy calves (OR: 2.16, 95% CI: 1.26 to 3.83, P = 0.004). A good agreement between PanCoV-RT-PCR and BCoV-RT-PCR to detect BCoV was identified (κ = 0.68, 95% CI: 0.392 to 0.967; P < 0.001). CONCLUSIONS AND CLINICAL IMPORTANCE: BCoV was more likely to be detected in diarrheic than healthy calves. The PanCoV-RT-PCR assay can be a useful tool to detect CoV samples from diarrheic 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".