PRESTINE: The Pan-Canadian REspiratory STandards INitiative for Electronic Health Records
Bibliographic record
Abstract
Background: PRESTINE was established to facilitate evidence-based clinical care, surveillance, and benchmarking. Aim: To identify and define respiratory data elements for EHRs that support and enable adherence with asthma guidelines. Methods: Potential data elements were based on a draft information model, including 425 elements in 28 categories. A Delphi Panel 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). In Round 2, the WG voted on elements lacking consensus (defined as a simple majority) in Round 1. In Round 3 (a facilitated face-to-face meeting), whole group consensus on elements and data definitions is being sought. Results: Thirty-five redundant core elements were collapsed. After 2 rounds, consensus was achieved on 333 of the remaining 390 elements (86%)(Table 1), including trigger exposures, allergies, diagnostic tests, laboratory results, education provided, and asthma control. Round 3 results will address contentious elements and data definitions. Table 1 Conclusions: This standardization process will establish a common approach to defining respiratory elements that support primary and tertiary care for asthma, and spirometry documentation, while simultaneously enabling 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 | 0.099 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".