DO VISIBILITY AIDS REDUCE THE RISK OF MOTOR-VEHICLE INJURY IN BICYCLISTS?
Bibliographic record
Abstract
Background There is limited literature regarding the effectiveness of visibility aids (eg, reflectors, lights, fluorescent clothing) in reducing the risk of a bicyclist- motor-vehicle (MV) collision. Objectives To determine if visibility aids reduce the risk of a bicyclist-MV collision. Methods Cases were bicyclists who were struck by a MV and assessed at emergency departments (EDs) from May 2008-October 2010 in Calgary and Edmonton, Alberta, Canada. Controls were bicyclists with non-MV injuries from the same EDs over the same time period. Participants were interviewed about their personal and injury characteristics, including use of visibility aids (clothing colour or reflective clothing, bike reflectors, etc). Injury information was collected from charts. ORs and 95% CIs were estimated for visibility aids after adjustment for confounders using logistic regression. Results There were 2403 injured bicyclists with 278 MV cases. The risk of a bicyclist-MV collision increased with age. Commuting also increased the odds of MV collision (OR 5.8; 95% CI 4.5 to 7.5). After accounting for location speed limit, bicyclist speed, and previous injury, white (OR 0.25; 95% CI 0.07 to 0.9) or other coloured (OR 0.45; 95% CI 0.2 to 0.9) compared with black clothing on the upper body reduced the odds of collision. Fluorescent clothing was associated with MV collisions (OR 1.7; 95% CI 1.0 to 2.8), even after adjusting for commuting and bicycling location. Significance Clothing choice may be important in reducing the risk of MV collision; however, factors beyond the individual also need to be examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".