A cluster randomized controlled trial of an electronic medical record–based pathway for pediatric asthma in primary care in Alberta
Bibliographic record
Abstract
BACKGROUND: Primary care physicians, who manage the care of most children with asthma, often do not optimally assess disease control, prescribe asthma controller medications, or provide family asthma education. We developed a pediatric asthma clinical pathway embedded in an electronic medical record (EMR) for use by primary care practices, and we sought to evaluate its effects on prescription and use of asthma controller medication for children with asthma. METHODS: We conducted a cluster randomized controlled trial, enrolling primary care practices in Alberta that used Wolf or Med Access EMRs, and managed at least 50 children with asthma. The multifaceted intervention included an EMR-based pathway for pediatric asthma, Web-based education modules for physicians, and train-the-trainer sessions for practice staff to provide patient education. The control intervention was standard care. We extracted study data from participating practices' EMRs, and standard emergency department and hospital administrative data sets. The primary and main secondary outcomes were improvement in the proportion of children prescribed and dispensed controller medications, respectively. RESULTS: Eleven practices were randomly assigned to each of the intervention and control groups. The intervention did not significantly change the proportion of children prescribed (mean difference 4.3%, 95% confidence interval [CI] -2.0% to 10.5%) or dispensed (mean difference -0.1%, 95% CI -7.1% to 6.9%) controller medications. INTERPRETATION: Our multifaceted intervention did not improve the proportion of children in primary care who were prescribed or dispensed a controller medication for asthma. These results suggest that such interventions may require active alerts and targeting of walk-in and urgent care clinics to have a meaningful impact on clinical practice. TRIAL REGISTRATION: Clinicaltrials.gov NCT02481037.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".