Effects of tulathromycin on incidence of various diseases and growth of young heifers
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of administration of 1 dose of tulathromycin on the incidence of various diseases and growth, identify risk factors for slow growth, and determine the association of Mycoplasma bovis status with the incidence of otitis media in calves. DESIGN: Randomized controlled trial and cross-sectional study. ANIMALS: 788 dairy heifer calves (median age, 3 days). PROCEDURES: Calves received tulathromycin or a saline (0.9% NaCl) solution control treatment once. Calves were observed daily for 8 weeks by farm staff to detect diseases. Nasal swab specimens were collected from some calves for Mycoplasma spp culture. RESULTS: Tulathromycin-treated calves had significantly lower odds of developing otitis media (OR, 0.41; 95% confidence interval, 0.58 to 0.82) versus control calves. Control calves had significantly higher odds of developing diarrhea (OR, 1.8; 95% confidence interval, 1.2 to 2.6) versus tulathromycin-treated calves. Control calves and those with failure of passive transfer, fever, lameness, respiratory tract disease, or diarrhea had significantly lower average daily gain versus other calves. Seventeen of the 66 (26%) calves that underwent repeated testing had positive Mycoplasma spp culture results, but positive results were not associated with otitis media. One of 42 calves with otitis media tested for Mycoplasma spp had positive results, and 1 of 43 age-matched calves without otitis media had positive results. CONCLUSIONS AND CLINICAL RELEVANCE: Tulathromycin-treated calves in this study had a lower incidence of diarrhea and otitis media versus control calves. Various diseases had negative effects on average daily gain. Mycoplasma bovis status was not associated with otitis media in calves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".