Determining rates of overweight and obese status in children using electronic medical records: Cross-sectional study.
Bibliographic record
Abstract
OBJECTIVE: (Electronic Medical Record Administrative data Linked Database). DESIGN: Heights and weights were abstracted for children 0 to 19 years of age who had at least one well-child visit from January 2010 to December 2011. Using the most recent visit, the proportions and 95% CIs of patients defined as overweight and obese were compared by age group, sex, and visit type using the World Health Organization growth reference standards. SETTING: Ontario. PARTICIPANTS: Children 0 to 19 years of age who were rostered to a primary care physician participating in EMRALD and had at least one well-child visit from January 2010 to December 2011. MAIN OUTCOME MEASURES: Proportion and 95% CI of children with overweight and obese status by age group; proportion of children with overweight and obese status by sex (with male sex as the referent) within each age group; and proportion of children with overweight and obese status at the most recent well-child visit type compared with other visit types by age group. RESULTS: < .05) had higher obesity rates compared with girls. Rates of overweight and obese status were lower using data from well-child visits compared with other visits. CONCLUSION: Electronic medical records might be useful to conduct population-based surveillance of overweight or obese status in children. Methodologic standards, however, should be developed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".