Bibliographic record
Abstract
Objective: To examine national trends in mortality rates for injuries among Canadian children younger than 15 years in 1979–2002. Methods: Data on injury deaths were obtained from the Canadian Vital Statistics system at Statistics Canada. Injuries were classified using the codes for external cause of injury and poisoning (E-codes) by intent and by mechanism. Mortality rates were age adjusted to the 1990 world standard population. Negative binomial regression was used to estimate the secular trends. Results: Annual mortality rates for total and unintentional injuries declined substantially (from 23.8 and 21.7 in 1979 to 7.2 and 5.8 in 2002, respectively), whereas suicide deaths among children aged 10–14 showed an increasing trend. All Canadian provinces and territories showed a decreasing trend in mortality rates of total injuries. Motor vehicle related injuries were the most common cause of injury deaths (accounted for an average of 36.4 % of total injury deaths), followed by suffocation (14.3%), drowning (13.5%), and burning (11.1%); however, suffocation was the leading cause for infants. The number of potential years of life lost due to injury before age 75 decreased from 89 343 in 1979 to 27 948 in 2002 for children aged 0–14 years. Conclusions: During the period 1979–2002, there were dramatic decreases in childhood mortality for
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.013 |
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".