Emergency department injury surveillance and aetiological research: bridging the gap with the two-stage case–control study design
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the two-stage case-control study design and its potential application to ED injury surveillance data and to apply this approach to published ED data on the relation between brain injury and bicycle helmet use. METHODS: Relevant background is presented on injury aetiology and case-control methodology with extension to the two-stage case-control design in the context of ED injury surveillance. The design is then applied to data from a published case-control study of the relation between brain injury and bicycle helmet use with motor vehicle involvement considered as a potential confounder. Taking into account the additional sampling at the second stage, the adjusted and corrected odds ratio and 95% confidence interval for the brain injury-helmet use relation is presented and compared with the estimate from the entire original dataset. Contexts where the two-stage case-control study design might be most appropriately applied to ED injury surveillance data are suggested. RESULTS: The adjusted odds ratio for the relation between brain injury and bicycle helmet use based on all data (n = 2833) from the original study was 0.34 (95% CI 0.25 to 0.46) compared with an estimate from a two-stage case-control design of 0.35 (95% CI 0.25 to 0.48) using only a fraction of the original subjects (n = 480). CONCLUSION: Application of the two-stage case-control study design to ED injury surveillance data has the potential to dramatically reduce study time and resource costs with acceptable losses in statistical efficiency.
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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 | 0.260 | 0.339 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".