Dog walking-related injuries of the hand and wrist: a systematic review
Bibliographic record
Abstract
BACKGROUND: Dog walking creates the risk of falls which can lead to musculoskeletal injuries. OBJECTIVES: This review aims to evaluate the epidemiology of dog walking-related hand and wrist injuries, discuss their estimated economic impact, and identify gaps in research and legislation. METHODS: Embase, Web of Science, PubMed, CINAHL, and Scopus databases were searched. Outcomes of interest were the incidence of dog-related hand and wrist injuries. The quality of studies was analysed using the Newcastle-Ottawa Scale (NOS). RESULTS: Five studies consisting of 491 400 injuries among 491 373 patients were included. Among these, 364 904 (74.3%) were female, and at least 152 247 (31.0%) were older than 65 years of age. A total of 110 722 specific fractures or soft tissue injuries to the hand and wrist were reported. Finger fractures were the most common injury among hand and wrist injuries (n=34 051; 30.8%). Being pulled by a leash was the most common cause of a direct dog-related injury (n=314 189; 68.5%). CONCLUSIONS: This review highlights a significant number of dog walking-related hand and wrist injuries, particularly in the elderly and female population. While finger fractures were the most frequently reported injury, the cost analysis in this review focused on distal radius fractures due to their substantial economic impact. We estimated the potential annual cost of dog walking-related wrist fractures in the UK to exceed £23 million. Preventative measures, including safer leash practices and public safety guidance, should be implemented to reduce injury risk.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".