P186 Facilitators to recruiting COPD patients to an adherence intervention trial
Bibliographic record
Abstract
Introduction and Objectives Recruitment rates to clinical trials in primary care are often lower than anticipated, even in successful trials. Researchers can be too optimistic about the number of eligible patients, and the GPs’ time and ability to recruit patients. The MAGNIFY cluster randomised trial is investigating the effect of a technologically-supported adherence package (Ultibro Breezhaler + adherence support technology) for COPD patients in primary care. This abstract explores facilitators for recruiting patients into a GP practice-level intervention trial. Methods Eligible practices willing to implement the adherence package were invited to participate. Algorithms run on electronic medical records (EMR) of intervention arm practices identified COPD patients aged ≥40 years, with ≥2 moderate/severe exacerbations in the last two years and ≤50% adherence to mono/dual therapy. Pharmacists were funded to recruit patients over a maximum of two calls; initial calls entailed a remote review and invitation to use the technology if suitable, with offer of a second to support device set-up and resolve problems. Results To date, 398/672 (59.2%) potentially eligible patients have been reviewed. 67/398 (16.8%) were deemed clinically unsuitable during the initial call. Of the remaining 331 patients, 218 (96.1%) accepted the support package; giving an overall recruitment rate onto the intervention of 54.8% (218/398 patients). Conclusions The recruitment rate was higher than many primary care trials. Recruitment of primary care patients into intervention trials may benefit from using cluster randomised trial designs and adopting novel approaches including EMR searches and dedicated pharmacists to identify, screen and recruit potential patients.
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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 | 0.128 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.008 |
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".