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Evaluating the efficacy of internal teat sealants at dry-off for the prevention of new intra-mammary infections during the dry-period or clinical mastitis during early lactation in dairy cows: A systematic review update and sequential meta-analysis

2023· review· en· W4317567319 on OpenAlexaff
Sydney D. Pearce, E. Jane Parmley, Charlotte B. Winder, Jan M. Sargeant, M Prashad, M Ringelberg, M Felker, D.F. Kelton

Bibliographic record

VenuePreventive Veterinary Medicine · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineMastitisPopulationMeta-analysisCullingIce calvingIncidence (geometry)Dairy cattleRelative riskLactationHerdVeterinary medicineInternal medicineAnimal scienceConfidence intervalEnvironmental healthPregnancyBiology

Abstract

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A systematic review and Bayesian sequential pair-wise meta-analyses were conducted to assess the efficacy of internal teat sealants (ITS) administered at dry-off in comparison to no treatment for preventing new intramammary infections (IMI) and clinical mastitis (CM) in dairy cattle. This work updated a previous systematic review and network meta-analysis conducted in 2019 but employed a narrowed scope and eligibility. The updated eligibility included studies that used ITS without concurrent therapy compared to a no treatment control (NTC), a study population of dairy cows or prepartum heifers, controlled trial design, and assessed one of the following outcomes: incidence of new IMI at calving or CM during the first 30 days in milk (DIM). Risk of bias was assessed through the Cochrane Risk of Bias 2.0 tool. Evidence quality was assessed using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). There were 141 potentially relevant records identified from the updated search conducted on April 29, 2021, with a publication date restriction of 2018 or later; one study passed full-text screening and was included. Of the 32 studies included in the previous review, 12 studies were relevant after applying the modified eligibility criteria, totaling 13 studies included in this review (12 addressing IMI at calving outcome, 4 addressing CM at 30 DIM outcome). Sequential meta-analysis was conducted for both outcomes in R 3.6.0. Decisions for stopping were assessed at each analysis for intervention effect or futility in finding an effect based on a priori minimum clinically relevant values (OR δ =0.5, 0.75). ITS at dry-off significantly reduced odds of new IMI at calving compared to NTC at the second meta-analysis (OR 2 =0.27, 95% CI=0.22–0.34), and onward (OR 12 =0.29, 95% CI=0.27–0.32). For CM at 30 DIM, significance was reached at the second meta-analysis (OR 2 =0.59, 95% CI=0.47–0.73), and onward (OR 3 =0.47, 95% CI=0.42–0.51). Stopping for effect occurred at the second analysis in both outcomes and OR δ s, but low-quality evidence and heterogeneity concerns were noted. A continuity-correction to include zero-event CM studies showed significance at the third meta-analysis (OR 3 =0.79, 95% CI=0.73–0.86), stopping for effect at the fourth for OR δ = 0.75 (OR 4 =0.77, 95% CI=0.72–0.83), and stopping for futility at the second for OR δ = 0.5 (OR 2 =0.94, 95% CI=0.75–1.20), but the main CM analysis was considered more appropriate due to the sensitivity analysis’ very low-quality evidence assessment. Based on sequential evidence available, sufficient research currently exists for practical use, and cessation of future research until substantial changes to ITS application occur may be appropriate. • Internal teat sealants reduce new intramammary infections and clinical mastitis. • Sequential meta-analysis assesses cumulative knowledge with each new publication. • Sequential meta-analysis can identify research stopping recommendations. • Efficacy and stopping recommendations became consistent for both outcomes early on. • Research should target other questions until industry products or practices change.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.435
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.340
GPT teacher head0.472
Teacher spread0.132 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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