A look at the incidence and risk factors for dog bites in unincorporated Harris County, Texas, USA
Bibliographic record
Abstract
AIM: This study examined the incidence, demographic predictors, and map patterns of dog bites to humans in unincorporated Harris County, Texas, USA. MATERIALS AND METHODS: Dog bites reported to Harris County Veterinary Public Health (HCVPH) between January 1, 2013, and December 31, 2016, were analyzed in this retrospective cohort study. Canine and victim characteristics and bite circumstances were evaluated to establish risk factors for bites. Geographic location was used to produce choropleth maps. RESULTS: There were 6683 dog bites reported to HCVPH between the years of 2013 and 2016, with stable incidence rates over time. The incidence was highest for both children and older adults. Dogs with the primary breed of Pit Bull had the greatest frequency of bites (25.07%), with the second highest breed being Labrador Retrievers (13.72%). Bites were more common from intact dogs of both genders, especially from intact males. Persons aged 70+ had the greatest incidence of severe injury (14.09/100,000). A strong correlation between dog bite incidences and stray dogs was found after controlling for the human population and income. CONCLUSION: Dog bites remain a largely preventable issue, and risk factors identified in this study can help direct preventative efforts to reduce the incidence of dog bites.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".