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Record W4393997422 · doi:10.2460/javma.23.11.0608

Leash-related injuries associated with dog walking: an understudied risk for dog owners?

2024· article· en· W4393997422 on OpenAlexaff
Rodrigo César Rosa, Richard Buckley

Bibliographic record

VenueJournal of the American Veterinary Medical Association · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsDemographicsIncidence (geometry)MedicineBreedInjury preventionRisk factorEnvironmental healthDemographyPoison controlPathologyBiologyEcology

Abstract

fetched live from OpenAlex

The topic of dog-walking injuries has recently gained notoriety through major media outlets in North America, including the Washington Post, NPR, and US News and World Report. In this review, we have compiled data from the main studies published in the past 2 decades that assess the incidence, demographics, and injury patterns related to dog leash walking. The available papers indicate that dog leash-related injuries have increased, particularly among women over 65. The most common causes of injury are dog-pulling behavior, which can result in tripping or tangling, with or without a fall, as well as upper extremity injuries. However, there is a lack of information regarding dog size, breed, training status, the type of leash used at the time of injury, and the role each factor may have in the incidence of injuries. The available data did not allow for evaluation of the impact of weather conditions on injury incidence. Information about the involved dogs, type of lead device, and weather conditions could be useful in identifying risk factors associated with dog ownership and guide prospective pet owners and their families to mitigate the risk of injuries.

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.412
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.300
Teacher spread0.283 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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