Leash-related injuries associated with dog walking: an understudied risk for dog owners?
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".