A Systematic Review on the Impact of Vaccination for Respiratory Disease on Antibody Titer Responses, Health, and Performance in Beef and Dairy Cattle
Bibliographic record
Abstract
Bovine respiratory disease (BRD) is a multifactorial disease complex commonly affecting beef and dairy operations. Vaccination against major BRD-related pathogens is routinely performed for disease prevention; however, uniform reporting of health and performance outcomes is infrequent. Our objective was to evaluate the effect of commercially available BRD-pathogen vaccination on titer response with respect to health or performance in beef and dairy cattle. This study was conducted under Prisma 2020 guidelines for systematic reviews and PRESS guidelines utilizing five databases. Criteria for study inclusion were as follows: research conducted in the USA or Canada, between 1982 and 10 October 2022, on beef or dairy cattle, using a commercially available vaccine labeled for a respiratory pathogen of interest, which evaluated antibody titers alongside either performance or morbidity. A total of 3020 records underwent title and abstract evaluation. Full-text analysis was conducted on 466 reports; 101 studies were included in the final review. Approximately 74% of included studies were beef cattle-based versus 26% dairy cattle-based. This review aimed to assess how vaccination titer responses affect beef and dairy cattle health and performance, but varying study methods made comparisons difficult, highlighting the need for consistent reporting.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".