Time for Lifestyle Medicine to Take Injury Prevention Seriously
Bibliographic record
Abstract
Over 2 decades ago, the United States National Academy of Sciences described injuries as "the most under-recognized major public health problem facing the nation." Our progress since then has been limited. Injuries still account for nearly 1 out of every 10 deaths in the world, and the global burden of injury is projected to increase over the next decade, predominately in low- and middle-income countries. Despite this, injury prevention receives scant attention from legislators, the education system, and, most strikingly, the health care system. The lifestyle medicine community, however, is beginning to focus on injury prevention and will play an increasing role in helping control the burden of injury. Lifestyle medicine practitioners are in a tremendous position to promote injury prevention. Physical activity and positive lifestyle changes can be accompanied with an increased focus on preventing injury. Lifestyle medicine can prevent injuries by supporting legislation, advancing medical advocacy, providing community education, and linking clinical care with injury prevention.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.019 | 0.039 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".