Implementation of a cystic fibrosis lung transplant referral patient decision aid in routine clinical practice: an observational study
Bibliographic record
Abstract
BACKGROUND: The decision to have lung transplantation as treatment for end-stage lung disease from cystic fibrosis (CF) has benefits and serious risks. Although patient decision aids are effective interventions for helping patients reach a quality decision, little is known about implementing them in clinical practice. Our study evaluated a sustainable approach for implementing a patient decision aid for adults with CF considering referral for lung transplantation. METHODS: A prospective pragmatic observational study was guided by the Knowledge-to-Action Framework. Healthcare professionals in all 23 Canadian CF clinics were eligible. We surveyed participants regarding perceived barriers and facilitators to patient decision aid use. Interventions tailored to address modifiable identified barriers included training, access to decision aids, and conference calls. The primary outcome was >80% use of the decision aid in year 2. RESULTS: Of 23 adult CF clinics, 18 participated (78.2%) and 13 had healthcare professionals attend training. Baseline barriers were healthcare professionals' inadequate knowledge for supporting patients making decisions (55%), clarifying patients' values for outcomes of options (58%), and helping patients handle conflicting views of others (71%). Other barriers were lack of time (52%) and needing to change how transplantation is discussed (42%). Baseline facilitators were healthcare professionals feeling comfortable discussing bad transplantation outcomes (74%), agreeing the decision aid would be easy to experiment with (71%) and use in the CF clinic (87%), and agreeing that using the decision aid would not require reorganization of the CF clinic (90%). After implementing the decision aid with interventions tailored to the barriers, decision aid use increased from 29% at baseline to 85% during year 1 and 92% in year 2 (p < 0.001). Compared to baseline, more healthcare professionals at the end of the study were confident in supporting decision-making (p = 0.03) but continued to feel inadequate ability with supporting patients to handle conflicting views (p = 0.01). CONCLUSION: Most Canadian CF clinics agreed to participate in the study. Interventions were used to target identified modifiable barriers to using the patient decision aid in routine CF clinical practice. CF clinics reported using it with almost all patients in the second year.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".