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Record W2738670062 · doi:10.3168/jds.2016-12512

Invited review: A systematic review and qualitative analysis of treatments other than conventional antimicrobials for clinical mastitis in dairy cows

2017· review· en· W2738670062 on OpenAlexafffund
David Francoz, Vincent Wellemans, Jeanine Dupré, Jean‐Philippe Roy, France Labelle, P. Lacasse, Simon Dufour

Bibliographic record

VenueJournal of Dairy Science · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsAgriculture and Agri-Food CanadaValacta (Canada)Université de Montréal
FundersAgriculture and Agri-Food CanadaUniversité de MontréalDalhousie UniversityDairy Farmers of Canada
KeywordsMastitisMedicineAntimicrobialObservational studySystematic reviewHomeopathyClinical trialData extractionInternal medicineMEDLINEAlternative medicinePathologyBiology

Abstract

fetched live from OpenAlex

Clinical mastitis is an important disease in dairies. Its treatment is mainly based on the use of antimicrobial drugs. Numerous non-antimicrobial drugs and treatment strategies have already been reported for clinical mastitis treatment, but data on their efficacy have never been collated in a systematic way. The objective of this systematic review was to identify treatments other than conventional antimicrobials for the treatment of clinical mastitis in lactating dairy cows. A systematic review was performed with studies written in English or French selected from CAB Abstracts, PubMed, and Web of Science from January 1970 to June 2014. Controlled clinical trials, observational studies, and experimental challenges were retained. Lactating dairy cows with clinical mastitis were the participant of interest. All treatments other than conventional antimicrobials for clinical mastitis during lactation were retained. Only studies comparing the treatment under investigation to a negative or positive control, or both, were included. Outcomes evaluated were clinical and bacteriological cure rates and milk production. Selection of the study, data extraction, and assessment of risk of bias was performed by 3 reviewers. Assessment of risk of bias was evaluated using the Cochrane Collaboration tool for systematic review of interventions. A total of 2,451 manuscripts were first identified and 39 manuscripts corresponding to 41 studies were included. Among these, 22 were clinical trials, 18 were experimental studies, and 1 was an observational study. The treatments evaluated were conventional anti-inflammatory drugs (n = 14), oxytocin with or without frequent milk out (n = 5), biologics (n = 9), homeopathy (n = 5), botanicals (n = 4), probiotics (n = 2), and other alternative products (n = 2). All trials had at least one unclear or high risk of bias. Most trials (n = 13) did not observe significant differences in clinical or bacteriological cure rates in comparison with negative or positive controls. Few studies evaluated the effect of treatment on milk yield. In general, the power of the different studies was very low, thus precluding conclusions on noninferiority or nonsuperiority of the treatments investigated. No evidence-based recommendations could be given for the use of an alternative or non-antimicrobial conventional treatment for clinical mastitis. However, probiotics and oxytocin with or without frequent milk out should not be recommended. We concluded that homeopathic treatments are not efficient for management of clinical mastitis.

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 imitation

Not 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.

metaresearch head score (Codex)0.042
metaresearch head score (Gemma)0.159
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.042
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.159
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0120.010
Bibliometrics0.0240.020
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.262
GPT teacher head0.484
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations54
Published2017
Admission routes2
Has abstractyes

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