Bibliographic record
Abstract
Police-reported data from Transport Canada’s National Collision Database (NCDB) are analyzed with a view to identify and quantify various factors that can impact the survivability of cyclists involved in a motor vehicle collision. A Least Absolute Shrinkage and Selection Operator (LASSO) regression and a multiple imputation (MI) process address the variable selection and missing data problems, respectively. The resulting probabilistic model suggests that collision sur-vivability depends largely on the cyclist’s age and helmet usage. Survivability improves with age up to age 21, peaks for cyclists aged 21 to 34, and falls after age 35. Controlling for age and other factors, a bicycle helmet reduces the risk that a cyclist fatality will occur by approximately 34% (OR: 0.66, 95% CI: 0.56-0.78). Survivability in general, and the apparent safety benefits of bicycle helmets in particular, do not appear to depend on the sex of the cyclist once the type of collision and other factors are controlled for. Head-on and rear-end collisions tend to be more deadly. Certain environmental and situational variables, like strong winds and traffic control devices, also appear to impact survivability. There might be opportunities to sensitize cyclists of various age groups about the risks they are exposed to while cycling, and prevent or better protect cyclists from head-on and rear-end collisions.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".