Using EMR data to evaluate a physician-developed lifestyle plan for obese patients in primary care.
Bibliographic record
Abstract
OBJECTIVE: To use primary care electronic medical records (EMRs) to evaluate the effects of a lifestyle intervention delivered to obese patients compared with obese patients who did not receive the intervention. DESIGN: Retrospective cohort analysis using EMR data derived from the Canadian Primary Care Sentinel Surveillance Network. SETTING: A primary care clinic in rural Alberta. PARTICIPANTS: Obese adult patients with at least 1 weight measurement in the time periods before and after the intervention, grouped by patients who received the intervention (n = 68) and those who did not (n = 365). INTERVENTION: Physician-developed lifestyle plan to address obesity through a variety of health-promoting recommendations. MAIN OUTCOME MEASURES: Mean change from before the intervention for weight, blood pressure, glycated hemoglobin A1c level, and body mass index measurements, compared between the control and intervention groups. RESULTS: Negligible weight change was observed in both groups, with the exception of older male patients (65 years and older) receiving the intervention, who lost significantly more weight than older men in the control group (a difference in mean reduction of 3.02 kg in favour of the intervention; P = .008). No overall group differences were seen in the secondary health outcomes, except for reductions in systolic and diastolic blood pressures in the intervention group (P = .002 and P = .04, respectively). Only the difference in systolic blood pressure remained significant after adjusting for covariates (P = .01). CONCLUSION: Providing real-time feedback about clinical interventions is possible using EMR data. Although the lifestyle intervention was associated with significant weight loss for a specific group of patients only, with the use of EMR data the cohort can be followed over time and additional health outcomes can be monitored. There is potential for individual physicians and practices to assess and improve clinical processes and interventions in a rigorous, timely, and manageable way.
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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 | 0.019 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".