Childhood Behavior Disorders and Injuries Among Children and Youth: A Population-Based Study
Bibliographic record
Abstract
CONTEXT: While an association between pediatric behavioral disorders and injuries is generally acknowledged, no studies have measured the risk for injury in the context of a large, population-based study that is free of cohort biases. OBJECTIVES: To examine the association between childhood behavior disorders ([CBDs] as indicated by prescription for methylphenidate [MPH]) and a variety of injury outcomes, and to evaluate the risk for injury among these children after controlling for known demographic correlates. DESIGN: Population-based database analysis of all children in British Columbia (BC) under the age of 19 as of December 31, 1996; comparison of those who had been prescribed MPH and therefore placed in the CBD group (n = 16, 806) and those who were not (n = 1,010,067). Demographic information collected was as follows: age, sex, measures of socioeconomic status, and region of residence. OUTCOME MEASURES: Common types of childhood injury in BC: International Classification of Diseases, Ninth Revision N-codes (fractures, open wounds, poisoning/toxic effect, concussion, intracranial, burns) and E-codes (falls, postoperative complications, motor vehicle accidents, struck by object, adverse effects of drugs, suffocation, drowning). DATA SOURCE: BC Linked Health Data Set and the BC Triplicate Prescription Program. RESULTS: After controlling for known demographic correlates, odds for injury was greater among those treated with MPH and presumed to have a behavioral disorder, when injury was characterized either by type (1.67; 99% confidence interval: 1.54-1.81) or cause (1.52; 99% confidence interval: 1.40-1.66) of injury. This increased risk extended to unexpected categories of injury such as postoperative complications and adverse effects of drugs. CONCLUSIONS: Children with CBDs have >1.5 times the odds of sustaining injuries of a variety of types from a variety of causes, even after controlling for known demographic correlates, than those without behavioral disorders. The risks for these children extend beyond those that might be directly associated with impulsivity and overactivity. Injury prevention strategies aimed at this group of children and youth would be beneficial. Policy-makers should account for increased risk of a wide variety of injuries in this group of children and youth.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".