Automated detection of motorcycle helmet use
Bibliographic record
Abstract
Road traffic collisions are among the top ten causes of death worldwide with more than 1.3 million deaths annually (WHO, 2018). Riders of motorised two- and three-wheelers are more vulnerable to injury and death and make up 28% of global road traffic deaths. In some regions, such as South-East Asia, this number is as high as 43% (WHO, 2018). Correct helmet use reduces the risk of death by 42% and the risk of head injuries by 62% (Liu, Ivers, Blows, Lo, & Norton, 2008). Increasing motorcycle helmet usage to close to 100% by 2030 has been identified as one of the twelve road safety targets by the Global Road Safety Partnership (WHO, 2018). Despite the clear benefits of wearing a helmet, increasing helmet use is challenging especially in low- and middle-income countries (LMICs). A large-scale helmet use media campaign in Thailand over five years showed no benefit (Patummasut, Phewchean, & Sirirattanapa, 2019). While legislating helmet use has shown a clear benefit, there is a disparity between the legislative benefit in high-income countries (HICs) compared to LMICs, with LMICs showing lower use of helmets and less reduction in brain injuries (Lepard, Spagiari, & Park, 2021).
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".