Body mass index and waist circumference documentation in Canadian primary care electronic medical records
Bibliographic record
Abstract
Abstract Background: Electronic medical records (EMR) are commonly used in primary care to document patient measurements including height and weight that are then used to produce body mass index (BMI) scores. However, little is known about the proportion of waist circumference (WC) documentation compared to BMI and the characteristics of patients with these measures. This study used a pan-Canadian research database, sourced from primary care EMRs, to describe BMI and WC documentation in primary care. Methods: A retrospective cohort design of primary care providers participating in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), this study presented descriptive, observational findings of EMR inputs. Frequencies and percentages of median BMI and WC documentation in CPCSSN EMRs and patient demographic characteristics are compared. Results: Of 707,819 Canadian patients aged of 40 or older, at least one BMI input was recorded for 58.6% and 11.5% had WC notations. The majority of patients (98.1%) with at least one WC measurement also had a BMI measurement while conversely 19.2% of patients with at least one BMI measurement also had a WC measurement. The most common median BMI category was overweight (36.9%) and median WC was 95.0 centimetres (IQR = 21.5).Conclusions: This study reports the documentation of obesity and overweight in select Canadian primary care EMRs infrequently recorded WC when compared to BMI. Future studies should examine the frequency and categories of anthropometric measurements in people with commonly managed chronic conditions and whether BMI and WC inputs are missing at random. Trial registration: Not applicable for this study.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".