Patterns of Injury in Children: A Population-Based Approach
Bibliographic record
Abstract
OBJECTIVE: We describe the frequency and patterns of injury affecting 96 359 children between 0 and 10 years old and living in Alberta, Canada. DESIGN: This population-based, longitudinal study involved children born in the 3 fiscal years of April 1, 1985 to March 31, 1988, recruited before age 1, and who remained in the study until at least age 5. We used the International Classification of Diseases, Ninth Revision, Clinical Modification chapter-17 diagnostic codes provided by physicians. Codes were grouped into 17 categories; injury episodes were calculated, and age- and gender-specific incidence rates for each category were calculated. The age, pattern, times of greatest risk, and the effect of gender on the type and incidence of injury were determined. SETTING: Health care administrative data were obtained from all fee-for-service health care venues in Alberta between April 1, 1985 and March 31, 1998 providing services to children registered with the Alberta Health Care Insurance Plan and otherwise meeting entrance criteria. RESULTS: Nearly 84% of children received care for an injury during the study period, and in any given year approximately 21% of the population studied had at least 1 injury. Repeat injury was common (73%), and boys were more likely than girls to be injured and to have repeat injury. The most common injuries were dislocations and sprains, open wounds, and superficial injuries and contusions. Burns, poisoning, intracranial injury, and foreign bodies were the next most common, and fractures were least common. Approximately 10% of injuries were multiple-category injuries. Rates varied greatly by injury category, age, and gender. Hospitalization rates varied in a similar manner and commonly accounted for approximately 10% of all services. Males were most likely to have an injury, and aboriginal children or children who had received welfare at some time were at greatest risk. CONCLUSIONS: Administrative data can be used to estimate the incidence of injury in a pediatric population. Distinct patterns of injury occur at different ages. Recurrent injury is common. Almost identical proportions of injury (46%) are treated in emergency departments and physicians' offices.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".