Bitten or struck by dog: A rising number of fatalities in Europe, 1995–2016
Bibliographic record
Abstract
We analyzed fatal dog attacks in Europe 1995-2016 using official death cause data from Eurostat. The data comprised the number of fatalities assigned The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) code W54 "bitten or struck by dog", which includes deaths due to direct attacks but which excludes many complications following dog bites, such as rabies. In 2016, dogs killed 45 Europeans, which translates to an incidence of 0.009 per 100,000 inhabitants. This is comparable to estimates from the USA (0.011), and Canada (0.007), but higher than Australia (0.004). The number of European fatalities due to dog attacks increased significantly at a rate of several percent per year. This increase could not be explained by increases in the human or the dog populations. By taking all fatalities reported 1995-2016 into account, we investigated the effects of age, gender and geography. First, children, including infants, were common victims, but also middle-aged and the elderly, while people between ages 10 and 39 were rarely killed by dogs. Second, boys and men were overrepresented, but only in certain age groups and in certain parts of Europe. Third, there were large national and regional differences, both in the effects of gender and in incidences, which ranged from 0 to 0.045 per 100,000 inhabitants. This study of dog-related fatalities at a European level is the first of its kind and forms a basis for more detailed, national studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".