Why a Decade of Road Traffic Safety?
Bibliographic record
Abstract
Each year, 1.24 million people die as a result of road traffic collisions around the world, and millions more are left to suffer the resultant disabilities of their nonfatal typically musculoskeletal injuries. The most productive members of society are the ones affected the most, and the subsequent economic impact cannot be ignored. Reducing the morbidity and mortality associated with road traffic injuries will reduce suffering and increase available resources that can be used more effectively. Road traffic injuries are preventable, and the impact of those that do occur can be mitigated. Adequate national and global funding, strategy, and measurable targets are fundamental to a sustainable response to road safety. Over the last decade, the United Nations and World Health Organization have been part of the gaining momentum toward addressing this issue, through resolutions and coordinating global efforts. This is what brought about the "Decade of Action for Road Traffic Safety," and as orthopaedic surgeons, our involvement is key for the collaborative public health response toward this effort.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".