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Record W2403460354 · doi:10.3168/jds.2016-10941

Prevalence and distribution of foot lesions in dairy cattle in Alberta, Canada

2016· article· en· W2403460354 on OpenAlexafffundabout
L. Solano, Herman W. Barkema, Steve Mason, Edmond A. Pajor, S.J. LeBlanc, Karin Orsel

Bibliographic record

VenueJournal of Dairy Science · 2016
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of GuelphUniversity of Calgary
FundersUniversity of Calgary
KeywordsHerdClawHoofDairy cattleVeterinary medicineSkin lesionAnimal scienceFoot (prosody)LamenessUdderMedicineLesionBiologyMastitisSurgery

Abstract

fetched live from OpenAlex

The objectives of this cross-sectional study were to determine the prevalence and distribution of foot lesions and associated cow- and herd-level risk factors in dairy cows in Alberta, Canada. Foot lesion data were recorded electronically by 7 hoof trimmers on 28,607 cows in 156 dairy farms from June 2009 to November 2012. Foot lesion prevalence estimates differed between farms that had the whole herd trimmed at once (≥80% of lactating cows were trimmed; n=69 farms and 8,020 cows) and farms on which part of the herd was trimmed (selection of cows was determined by farmer and <80% of lactating cows were trimmed; n=87 and 20,587 cows). Estimates were consistently higher for the latter likely because farmers presumably prioritized lame cows in partial-herd trims. On farms with whole-herd trims, digital dermatitis was the most common lesion among all housing types, present in 15% of cows and 94% of herds. Sole ulcers and white line disease were detected in 6 and 4% of the cows and 92 and 93% of herds, respectively. Other infectious and claw horn lesions each affected 1 to 2% of cows and 62 to 78% of herds. Intraclass correlation coefficients for hoof trimmers ranged from 0.01 to 0.20 for all lesions, indicating some clustering of recorded lesions by trimmer. Multilevel mixed logistic regression models were constructed (including hoof trimmer as fixed and farm as random effects) for the 3 most frequently identified lesions. Prevalence of digital dermatitis decreased with increasing parity, but this effect interacted with days in milk (DIM); primiparous cows had higher odds of digital dermatitis in mid lactation (100-199 DIM) and late lactation (≥200 DIM) compared with cows at other stages of lactation. In contrast, prevalence of sole ulcers and white line disease increased with increasing parity; compared with cows in parity 1, those in parity 4 had 5 or 7 times higher odds of having these lesions, respectively. Cows in mid lactation and late lactation had higher odds of sole ulcers and white line disease than cows at other stages of lactation, regardless of parity. Digital dermatitis prevalence was 2 times higher in herds housed in barns with access to an exercise area. The odds of sole ulcers and white line disease were ≥2 times higher in cows housed in freestalls than those housed in deep-bedded packs. Therefore, preventive measures for control of digital dermatitis merit emphasis, especially in primiparous cows and on farms with exercise areas. In addition, improving housing environment by providing a deep-bedded area for older cows in mid lactation or late lactation could reduce prevalence of claw horn lesions. We inferred that foot lesion data recorded by hoof trimmers can provide useful information not only to develop effective foot health programs at herd level, but also for disease surveillance and genetic improvement at regional and national levels.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.304
Teacher spread0.271 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations178
Published2016
Admission routes3
Has abstractyes

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