Changing epidemiology of assault victims in an emergency department participating in information sharing with police: a time series analysis
Bibliographic record
Abstract
INTRODUCTION: Violent injury places a large burden on the NHS. We had implemented information sharing in our ED in 2007 and aimed to see which patient groups were most affected by information sharing, as this would provide clues as to how this complex intervention works. METHODS: Retrospective time series study of all the assault victims presenting for ED care between 2005 and 2014 at a single ED in England. RESULTS: 10 328 patients presented during the study period. There was a 37% decrease in the number of patients presenting after assault, consistent with national trends. The proportions of people arriving by ambulance, and the proportion of men did not change during the study period. There were no important changes in the age of our assault patients in this study. Greater, disproportionate, decreases in rates of violence were seen in patients who presented at the weekend up (incidence rate ratio (IRR)=0.57, 95% CI 0.50 to 0.64) versus weekdays (IRR=0.72; 95% CI 0.62 to 0.83) There were also disproportionately greater decreases over the study period in patients who were discharged with no hospital follow-up (IRR=0.51, 95% CI 0.45 to 0.56) versus those leading to either an inpatient admission (IRR=1.05, 95% CI 0.84 to 1.31) or outpatient follow-up (IRR=1.23, 95% CI 0.93 to 1.64). CONCLUSIONS: The epidemiology of violent injury at our institution has changed over the last 10 years and is most marked in a reduction of visits at the weekend, and in those who leave without follow up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".