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Record W3007146537 · doi:10.1017/s1466252319000318

Comparative efficacy of antimicrobials for treatment of clinical mastitis in lactating dairy cattle: a systematic review and network meta-analysis

2019· review· en· W3007146537 on OpenAlexaff
Charlotte B. Winder, Jan M. Sargeant, Dongfang Hu, Chong Wang, D.F. Kelton, Michael A. Godkin, Katheryn J. Churchill, Annette M. O’Connor

Bibliographic record

VenueAnimal Health Research Reviews · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
Fundersnot available
KeywordsMastitisMedicineAntimicrobialMeta-analysisClinical trialPlaceboSystematic reviewRandomized controlled trialRelative riskIntensive care medicineInternal medicineMEDLINEConfidence intervalAlternative medicinePathologyBiology

Abstract

fetched live from OpenAlex

A systematic review and network meta-analysis were conducted to assess the relative efficacy of antimicrobial therapy for clinical mastitis in lactating dairy cattle. Controlled trials in lactating dairy cattle with natural disease exposure were eligible if they compared an antimicrobial treatment to a non-treated control, placebo, or a different antimicrobial, for the treatment of clinical mastitis, and assessed clinical or bacteriologic cure. Potential for bias was assessed using a modified Cochrane Risk of Bias 2.0 tool. From 14775 initially identified records, 54 trials were assessed as eligible. Networks were established for bacteriologic cure by bacterial species group, and clinical cure. Disparate networks among bacteriologic cures precluded meta-analysis. Network meta-analysis was conducted for trials assessing clinical cure, but lack of precision of point estimates resulted in wide credibility intervals for all treatments, with no definitive conclusions regarding relative efficacy. Consideration of network geometry can inform future research to increase the utility of current and previous work. Replication of intervention arms and consideration of connection to existing networks would improve the future ability to determine relative efficacy. Challenges in the evaluation of bias in primary research stemmed from a lack of reporting. Consideration of reporting guidelines would also improve the utility of future research.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.784
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0170.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.686
GPT teacher head0.599
Teacher spread0.087 · 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.

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

Citations15
Published2019
Admission routes1
Has abstractyes

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