Characteristics associated with pediatric growth measurement collection in electronic medical records: a retrospective observational study
Bibliographic record
Abstract
BACKGROUND: Complete growth measurements are an essential part of pediatric care providing a proxy for a child's overall health. This study describes the frequency of well-child visits, documented growth measurements, and clinic and provider factors associated with measurement. METHODS: Retrospective cross-sectional study utilizing electronic medical records (EMRs) from primary care clinics between 2015 and 2017 in Manitoba, Canada. This study assessed the presence of recorded height, weight and head circumference among children (0-24 months) who visited one of 212 providers participating in the Manitoba Primary Care Research Network. Descriptive and multivariable logistic regression analyses assessed clinic, provider, and patient factors associated with children having complete growth measurements. RESULTS: Our sample included 4369 children. The most frequent growth measure recorded was weight (79.2% n = 3460) followed by height (70.8% n = 3093) and head circumference (51.4% n = 2246). 67.5% of children (n = 2947) had at least one complete growth measurement recorded (i.e. weight, height and head circumference) and 13.7% (n = 599) had complete growth measurements at all well-child intervals attended. Pediatricians had 2.7 higher odds of documenting complete growth measures within well-child intervals compared to family physicians (95% CI 1.8-3.8). Additionally, urban located clinics (OR 1.7, 95% CI 1.2-2.5), Canadian trained providers (OR 2.3, 95% CI 1.4-3.7), small practice size (OR 1.6, 95% CI 1.2-2.2) and salaried providers (OR 3.4, 95% CI 2.2-5.2) had higher odds of documented growth measures. CONCLUSIONS: Growth measurements are recorded in EMRs but documentation is variable based on clinic and provider factors. Pediatric growth measures at primary care appointments can improve primary prevention and surveillance of child health outcomes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".