A Decision Aid for COPD patients considering inhaled steroid therapy: development and before and after pilot testing
Bibliographic record
Abstract
BACKGROUND: Decision aids (DA) are tools designed to help patients make specific and deliberative choices among disease management options. DAs can improve the quality of decision-making and reduce decisional conflict. An area not covered by a DA is the decision of a patient with chronic obstructive pulmonary disease (COPD) to use inhaled steroids which requires balancing the benefits and downsides of therapy. METHODS: We developed a DA for COPD patients considering inhaled steroid therapy using the Ottawa Decision Support Framework, the best available evidence for using inhaled steroid in COPD and the expected utility model. The development process involved patients, pulmonologists, DA developers and decision making experts. We pilot tested the DA with 8 COPD patients who completed an evaluation questionnaire, a knowledge scale, and a validated decisional conflict scale. RESULTS: The DA is a computer-based interactive tool incorporating four different decision making models. In the first part, the DA provides information about COPD as a disease, the different treatment options, and the benefits and downsides of using inhaled steroids. In the second part, it coaches the patient in the decision making process through clarifying values and preferences. Patients evaluated 10 out of 13 items of the DA positively and showed significant improvement on both the knowledge scale (p = 0.008) and the decisional conflict scale (p = 0.008). CONCLUSION: We have developed a computer-based interactive DA for COPD patients considering inhaled steroids serving as a model for other DAs in COPD, in particular related to inhaled therapies. Future research should assess the DA effectiveness.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".