Low-impact strategy for capturing better emergency department injury surveillance data
Bibliographic record
Abstract
OBJECTIVES: Injury prevention should be informed by timely surveillance data. Unfortunately, most injury surveillance only captures patients with severe injuries and is not available in real time, hampering prevention efforts. We aimed to develop and pilot a simple injury surveillance strategy that can be integrated into routine emergency department (ED) workflow to collect more robust mechanism of injury information at time of visit for all injured ED patients with minimal impact on workflow. METHODS: We reviewed ED injury surveillance systems and considered ED workflow. Forms were developed to collect injury-related information on ED patients and refined to address workload concerns raised by key stakeholders. Research assistants observed ED staff as they registered injured patients and noted the time required to collect data and any ambiguities or concerns encountered. Interobserver agreement was recorded. RESULTS: Injury surveillance questions were based on a modification of the International Classification of External Causes of Injury. Research assistants observed 222 injured patients being admitted by registration clerks. The mean time required to complete the surveillance form was 64.9 s (95% CI 59.9 s to 69.9 s) for paper-based forms (120 cases) and 44.5 s (95% CI 41.7s to 47.4s) with direct electronic data entry (102 cases). Interobserver agreement (26 cases) was 100% for intent (kappa=1.0) of injury and 96% for mechanism of injury (kappa=0.74). CONCLUSIONS: We report a simple injury surveillance strategy that ED staff can use to collect meaningful injury data in real time with minimal impact on workflow. This strategy can be adapted to enhance regional injury surveillance efforts.
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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 | 0.037 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".