Mortality in a cohort of 3.1 million children, adolescents and young adults
Bibliographic record
Abstract
BACKGROUND: Many youth deaths occur in the first year of life, from prematurity and anomalies. Detailing mortality after age 1 year may differentially guide preventive strategies in children, adolescents and young adults. METHODS: A cohort study in Ontario, Canada comprised 3 139 698 children born from 1990 to 2016. Adjusted HR (aHR) for death between 1 and 24 years were generated, comparing demographic variables and parity. RESULTS: After a median of 13.7 years of follow-up, 6930 deaths occurred between ages 1 and 24 years (incidence rate 17.0 per 100 000 person-years), peaking at age 23 years (43.7 per 100 000). The aHR for death was higher among males than females (1.44, 95% CI 1.37 to 1.51), rural versus urban areas (1.48, 95% CI 1.39 to 1.58), lowest versus highest income areas (1.39, 95% CI 1.29 to 1.51) and at parity 1 (1.16, 95% CI 1.10 to 1.23), parity 2 (1.34, 95% CI 1.23 to 1.45), parity 3+ (1.96, 95% CI 1.74 to 2.21), each relative to a child without an older sibling. Among males, the proportion of deaths due to injury jumped from 30% before age 15 years to 65% thereafter, and in females, from 28% to 51%. Intentional self-harm/assault explained 11% of injury-related deaths among males before age 15 years, and 20% thereafter, with respective figures of 18% and 17% for females. Deaths outside of hospital increased with age, from 35% at age 1 year, to 66% at age 22 years. CONCLUSION: There is a heightened susceptibility of dying starting at age 15 years, especially among males, from injury, and arising outside of hospital.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".