Pan-Canadian asthma and COPD standards for electronic health records: A Canadian Thoracic Society Expert Working Group Report
Bibliographic record
Abstract
RATIONALE: The Canadian Thoracic Society established a pan-Canadian respiratory standards initiative for electronic health records (EHRs) (PRESTINE).OBJECTIVE: We aimed to identify and define respiratory data elements for EHRs for asthma, COPD and related pulmonary function elements that enable adherence with respiratory best practice guidelines.METHOD: Potential data elements (n = 425) were based on a published asthma/COPD information model. Using modified RAND-UCLA Appropriateness and Delphi methods, a working group (WG) of 12 experts independently rated each element based on 4 domains (strength of evidence, clarity, relevance, feasibility) using a 5-point Likert Scale, plus an overall rating (include as core, optional or exclude). Subsequent independent voting rounds addressed elements lacking consensus (defined as 60% agreement) in previous rounds. A facilitated face-to-face meeting was convened during which WG consensus was sought. A list of included data element definitions and medications were sent for external stakeholder review.MAIN RESULTS: After 4 rounds of voting (including the face-to-face meeting), the WG identified 77 core and 23 optional elements for asthma, and 72 core and 21 optional elements for COPD. Of those, 53 core and 15 optional elements were common to both asthma and COPD. The list of asthma/COPD and smoking cessation medications included 40 products and 48 brands.CONCLUSIONS: This consensus initiative has identified asthma, COPD, and pulmonary function data elements and definitions as well as a list of medications recommended by experts for inclusion in EHRs to support primary and tertiary care for these diseases, and to enable outcomes monitoring, benchmarking and performance evaluation.
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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 | 0.123 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".