A patient decision aid for mild asthma: Navigating a new asthma treatment paradigm
Bibliographic record
Abstract
INTRODUCTION: In mild asthma, as-needed budesonide-formoterol offers similar protection from severe exacerbations as daily inhaled corticosteroids (ICS), with lower ICS exposure but slightly increased symptoms. We sought to develop an electronic decision aid to guide discussions about the pros and cons of these first-line options, while identifying and integrating user preferences. METHODS: Following International Patient Decision Aid Standards, we created a mild asthma decision aid prototype comparing convenience, clinical outcomes, cumulative ICS dose exposure, costs, and side-effects of each option. After face validation, the prototype was iteratively adapted through rapid-cycle development. Each cycle consisted of a patient focus group and a primary care physician interview. We made user preference-based improvements after each round, until reaching a pre-set stopping criterion (no new critical issues identified). We then performed a summative qualitative content analysis. RESULTS: Over 5 cycles, we recruited 21 asthma patients (12/21 women, 10/21 ≥ 60 years old) and 5 physicians. Serial changes included simplification and reduction of text and reading level, inclusion of an ICS "myths" section and elaboration of patient-friendly infographics for numerical comparisons. User preferences fell within Content, Format, and tool use Process themes. In response to decision-making preferences, we created a complementary one-page conversation aid for patient-provider use at the point-of-care. CONCLUSIONS: We present preference-based electronic patient decision and conversation aids for treatment of mild asthma. Our user preference analyses offer useful insights for development of such tools in other chronic diseases. These tools now require integration into point-of-care workflows for measurement of real-world uptake and impact.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".