Using EMRALD to assess baseline body mass index among children living within and outside communities participating in the Ontario, Canada Healthy Kids Community Challenge
Bibliographic record
Abstract
OBJECTIVES: The Healthy Kids Community Challenge is a large-scale, centrally-coordinated, community-based intervention in Ontario, Canada that promotes healthy behaviours towards improving healthy weights among children. With the goal of exploring tools available to evaluators, we leveraged electronic medical records from primary care physicians to assess child weights prior to launch of the Healthy Kids Community Challenge. This study compares the baseline (i.e. pre-intervention) prevalence of overweight and obesity in children 1-12 years of age living within and outside Healthy Kids Community Challenge communities. DESIGN: Cross-sectional analysis of a primary care patient cohort. SETTING: Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. PARTICIPANTS: A cohort of 19 920 Ontario children who are rostered to an EMRALD physician. Children were 1-12 years of age at a primary care visit with recorded measured height and weight, between January 1, 2014 and December 31, 2015. OUTCOME MEASURE: Overweight and obesity as determined by age- and sex-standardized body mass index using World Health Organization's Growth Standards. RESULTS: In Healthy Kids Community Challenge communities, 25.6% (95% CI 24.6-26.6%) of children had zBMI above normal (i.e. >1) compared to 26.7% (95% CI 25.9-27.5%) for children living outside of Healthy Kids Community Challenge communities. CONCLUSIONS: Despite some differences in sociodemographic characteristics, zBMI of children aged 1-12 years were similar inside and outside of Healthy Kids Community Challenge community boundaries prior to program launch.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".