Improving Documentation of Pediatric Height, Weight, and Body Mass Index by Primary Care Providers
Bibliographic record
Abstract
The regular documentation of anthropometric data in an electronic medical record (EMR) is one tracking method used by primary care providers to follow the growth trajectory and development of children in their health care practices. EMR reminders have been proposed as a method to increase recording of pediatric height and weight by primary care providers, leading to potentially better detection and management of children classified as overweight or obese. The aim of this pre-post study was to improve a Family Health Team's physician documentation of pediatric height and weight through the implementation of an EMR reminder alert tool. The documentation rate for children 4-7 years old in the 6 months before intervention was 36% of children seen. After implementation of EMR reminder alerts, primary care physicians' documentation rate rose to 45% (9% increase; P < 0.01), but it was below the 15% target increase. Better documentation of pediatric height and weight by family physicians is needed to improve monitoring of children's growth trajectories. Additional strategies to increase documentation rates are needed.
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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 | 0.005 | 0.028 |
| 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 | 0.000 | 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".