A systematic review of disease control strategies in beef cow–calf herds, part 2: preweaned calf morbidity and mortality associated with neonatal calf diarrhea and bovine respiratory disease
Bibliographic record
Abstract
Preventing neonatal calf diarrhea (NCD) and bovine respiratory disease (BRD) in cow-calf herds is essential to optimizing calfhood health. Disease control can prevent morbidity and mortality; however, evidence concerning the effectiveness of practices to achieve this is limited. The objective of this systematic review was to assess and summarize the evidence on the effectiveness of management practices to prevent calf morbidity and mortality from NCD and BRD in beef cow-calf herds. The population of interest was preweaned beef calves. The outcomes were calf morbidity and mortality caused by NCD and BRD. Only studies reporting naturally occurring diseases were included. Seventeen studies were deemed relevant, 6 studies of which were controlled trials or randomized controlled trials (RCTs), and 11 were observational studies. Most management practices had some evidence to support their use; however, the certainty of the findings was low to very low. Most of the practices were shown to impact both NCD and BRD. Yet, the different levels of consistency in the directionality of the findings suggest that some outcomes are more affected by some practices than others. More well-designed RCTs and cohort studies are required to provide reliable estimates to support recommended practices for cow-calf herds.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".