Invited review: Effectiveness of precalving treatment on postcalving udder health in nulliparous dairy heifers: A systematic review and meta-analysis
Bibliographic record
Abstract
Clinical mastitis affects 3% of primiparous dairy cattle (heifers) in the first month after calving. Additionally, the prevalence of intramammary infection (IMI) in the months before first calving is high, resulting in a high prevalence of heifers calving with IMI. Precalving therapy is an accepted recommendation for reducing mastitis in multiparous cows, but prophylactic treatment for heifers is uncommon in North America. Objectives of this study were to (1) quantify changes in postcalving udder health in heifers following application of a precalving treatment; (2) compare effectiveness among various types of treatments; and (3) compare effectiveness of various types of treatments against specific pathogens. A systematic review was conducted comparing interventions aimed at improving udder health in heifers. Of 62 included studies, 48 clinical trials were used in a meta-analysis. Data were synthesized using a random effects model for meta-analysis, followed by sub-group analyses comparing treatment types, and specific pathogens with statistical testing using meta-regression. Occurrence of mastitis (defined as elevated somatic cell count, clinical mastitis, and IMI) was reduced in treated heifers compared with untreated controls with a pooled risk ratio of treated to untreated heifers of 0.56 (95% confidence interval: 0.47 to 0.67). Upon stratification by treatment types, teat sealants and combination therapies (vaccines and antimicrobials; antimicrobials and teat sealants; and all 3) were most effective at improving udder health with pooled risk ratios of 0.40 (95% confidence interval: 0.30 to 0.52) and 0.34 (95% confidence interval: 0.25 to 0.45), respectively. Antimicrobials and vaccines also reduced occurrence of IMI and subclinical and clinical mastitis when compared with untreated heifers. Although variation was observed in the pathogen-specific effectiveness of treatments at reducing rates of disease, antimicrobials, teat sealants, and combinations of vaccines or teat sealants with antimicrobials were consistently effective, whereas vaccines were only effective for contagious pathogens. Recommendations for use of antibiotics should consider their relative benefit while also considering potential for increasing antimicrobial resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.020 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".