Identify the key characteristics of pedestrian collisions through in-depth interviews: a pilot study
Bibliographic record
Abstract
This study aimed to assess the feasibility of recruiting injured pedestrians from the emergency department of a major trauma centre, using an in-depth interview shortly post collision. Convenience sampling was used to prospectively recruit injured pedestrians from the Alfred Hospital Emergency and Trauma Centre. Of the 102 injured pedestrians, 39 met eligibility criteria and of these, 30 (77%) consented and completed the questionnaire. Over half of the collisions occurred at an intersection (57%), and of these the most common pre-impact vehicle manoeuvre was a vehicle turning into the street the pedestrian was crossing. In-depth interview during the early post-crash period was a feasible and effective method of collecting detailed data in an accessible sample. However, only 38% of patients met eligibility criteria. To enhance representativeness, supplementing interview data with police-reported crash data, recruiting from hospital wards and crash location assessment is recommended.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".