PREVENTING UNINTENTIONAL INJURY IN CHILDREN AND ADOLESCENTS: A CALL TO ACTION
Bibliographic record
Abstract
Objective To study prospectively all injuries in children and adolescents up to 16 years presenting to a regional emergency department, to ascertain detailed injury patterns and to use these data to recommend injury prevention priorities. Methods Prospective electronic injury surveillance collected over a 10-year period (1997–2007) in a hospital with a paediatric population of 75 000. The electronic dataset was based on the minimal dataset validated in Australia and Canada. All fatalities were obtained from the central statistics office. Results Over 10 years, there were 31 fatalities, 5408 admissions and 40 817 attendances due to injury. Males outnumbered females and 24 317 (60%) occurred at home. Peak injury presentations were between 18:00 and 20:00 hours. Minor injuries accounted for 32 456 (80%) of the total. Fractures resulting from high falls (n = 1194) tended to result from bunk beds, staircases, horses, walls and playground equipment. Burns (n = 630) involved hot liquids, hot bath water, hot cooking oil and hot cooking plates. Pedestrian injuries (n = 251) were predominantly “dart outs” in urban areas. Poisonings (n = 1153) were predominantly medicinal products and crush injuries (n = 485) related to finger entrapment in doors. Of 31 unintentional injury deaths, road-related accounted for 16 (52%), five were house fires, three were due to drowning, two due to poisoning and five to other causes. Conclusions Prevention priorities should focus on home injuries, hot liquid burns and scald injuries, finger entrapment in doors and high falls from walls, beds and playground equipment. To prevent road-related injuries and deaths, further legislation, urban planning and greater police enforcement is required.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".