Clinical trial simulations in pulmonary fibrosis: patient-focused insights and adaptations
Bibliographic record
Abstract
Background: Patient recruitment and retention are a challenge when conducting clinical trials in patients with pulmonary fibrosis, including idiopathic pulmonary fibrosis and other interstitial lung diseases. This study aimed to understand and address the barriers associated with trial participation for these populations. Methods: Nine patients, nine caregivers and three healthcare professionals participated in virtual simulations of planned phase III trials. During the simulations, participants received information about the trials and either tested a home spirometry device or watched a home spirometry demonstration, before providing their insights in debriefs. The findings were interpreted in advisory boards with representatives from patient organisations and expert investigators. Results: Regarding barriers to participation, patient fatigue and breathlessness were emphasised as posing challenges for travel, visit length and completion of onsite assessments. Lack of information, support and appreciation were also identified as factors that may exacerbate anxiety and negatively affect participant retention rates. Feedback on the home spirometry was mixed, with participants appreciating being able to complete the test at home but worrying about device handling. Based on the insights gained, patient-friendly adaptations were made to the trial protocol and conduct, including remote assessment of patient-reported outcomes, increased visit flexibility, travel support services, patient and caregiver information campaigns, and training of investigators on patients' needs. Conclusions: Participants identified important barriers to participation, which led to patient-friendly changes being made to the planned trials. As a result, participation in the planned trials should be less burdensome, with improved recruitment and retention rates, and ultimately, improved data quality.
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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 | 0.044 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".