Cohort Profile: Multimorbidity in Children and Youth Across the Life-course (MY LIFE) Study.
Bibliographic record
Abstract
OBJECTIVE: This manuscript serves to provide an overview of the methods of the Multimorbidity in Children and Youth across the Life-course (MY LIFE) study, profile sample characteristics of the cohort, and provide baseline estimates of multimorbidity to foster collaboration with clinical and research colleagues across Canada. METHOD: MY LIFE is comprised of 263 children (2-16 years) with a physical illness recruited from McMaster Children's Hospital, their primary caregiving parent, and their closest-aged sibling. Participants are followed with data collection at recruitment, 6, 12, and 24 months which includes structured interviews, self-reported measures, and biological samples and occur in a private research office or at participants' homes. Post-COVID-19, data collection transitioned to mail and telephone surveys. RESULTS: At recruitment, children were 9.4 (4.2) years of age and 52.7% were male. The mean duration of their physical illness was 4.5 (4.1) years; 25% represent incident cases (duration <1 year). Most (69.7%) had healthy body weight and intelligence in the average range (73.5%). Overall, 38.2% of children screened positive for ≥1 mental illness according to parent report (24.8% screened positive based on child self-report). Compared to 2016 Census data, the MY LIFE cohort overrepresents families of higher socioeconomic status. CONCLUSIONS: Multimorbidity is common among children and these baseline data will serve to measure relative changes in the mental health of children with physical illness over time. MY LIFE will provide new information for understanding multimorbidity among children, though underrepresentation of lower socioeconomic families may have implications for the generalizability of findings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".