Bibliographic record
Abstract
Evidence-Based Best Practices in Prevention of NeurotraumaEvery year, thousands of people suffer neurotrauma due to motor vehicle accidents, sports and playground injuries, and farm and occupational injuries.Although injury reduction targets have been established and indicators have been developed to measure progress in prevention, no method of examining and evaluating effective injury prevention practices has been readily available.This compendium aims to fill this gap by portraying exemplars that have the potential to reduce the incidence of these injuries, and by providing a detailed methodology that is effective in identifying innovative best practices.The intention of this work is not to be encyclopedic; rather, the authors have reviewed the twenty-eight best and promising practices, taking into consideration the complexity of injury dynamics, and analysed what constitutes a best practice as the shift is made from individual clinical practice to the collective practice associated with policy implementation at the community level.They have also provided unique coverage of age-specific practices, an upto-date bibliography, and a directory of the major programs and professionals.The first worldwide assessment of its kind, this work is an important contribution to the emerging field of unintentional-injury prevention.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.888 | 0.811 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".