Bibliographic record
Abstract
A recent UNICEF study of Organization of Economic Cooperation and Development (OECD) countries found that injury is the leading cause of death among children in each of the 26 countries examined (see CMAJ 2001; 164[10]:1483). The annual death rate due to injury is highest in Korea (25.6 per 100 000 children) and lowest in Sweden (5.2 per 100 000); the rate for Canadian children is 9.7 per 100 000. Traffic accidents accounted for 41% of child deaths by injury among OECD countries, with the highest rates being found in Greece (62%) and Italy (54%). The rate of child death due to traffic accidents was lowest in Mexico (30%) and Japan (36%). Traffic deaths represent 44% of all child deaths by injury in Canada. Drowning accounted for 15% of all child deaths by injury in the OECD, while 7% were attributed to fire, 4% to falls, 2% to poisoning and 1% to firearm accidents; other unintentional injuries account for 16% of deaths. Intentional injuries accounted for the remaining 14% of child deaths. Fire accounts for proportionately more child deaths in Canada than the OECD average (10% compared with 7%), while falls and drowning account for fewer (2% versus 4% for falls, and 13% compared with 15% for drowning). For the OECD as a whole, and for each country, boys are more likely than girls to die as a result of injury (15.9 per 100 000 vs. 9.2 per 100 000). This disparity between the sexes is most pronounced in Ireland, where the boy–girl ratio for injury-related death is 2.3:1, and least pronounced in Sweden, where the ratio is 1.34:1; the Canadian ratio is 1.61:1.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.086 | 0.024 |
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".