Strategies to Improve Recruitment to a De-escalation Trial: A Mixed-Methods Study of the OPTIMA Prelim Trial in Early Breast Cancer
Bibliographic record
Abstract
AIMS: De-escalation trials are challenging and sometimes may fail due to poor recruitment. The OPTIMA Prelim randomised controlled trial (ISRCTN42400492) randomised patients with early stage breast cancer to chemotherapy versus 'test-directed' chemotherapy, with a possible outcome of no chemotherapy, which could confer less treatment relative to routine practice. Despite encountering challenges, OPTIMA Prelim reached its recruitment target ahead of schedule. This study reports the root causes of recruitment challenges and the strategies used to successfully overcome them. MATERIALS AND METHODS: A mixed-methods recruitment intervention (QuinteT Recruitment Intervention) was used to investigate the recruitment difficulties and feedback findings to inform interventions and optimise ongoing recruitment. Quantitative site-level recruitment data, audio-recorded recruitment appointments (n = 46), qualitative interviews (n = 22) with trialists/recruiting staff (oncologists/nurses) and patient-facing documentation were analysed using descriptive, thematic and conversation analyses. Findings were triangulated to inform a 'plan of action' to optimise recruitment. RESULTS: Despite best intentions, oncologists' routine practices complicated recruitment. Discomfort about deviating from the usual practice of recommending chemotherapy according to tumour clinicopathological features meant that not all eligible patients were approached. Audio-recorded recruitment appointments revealed how routine practices undermined recruitment. A tendency to justify chemotherapy provision before presenting the randomised controlled trial and subtly indicating that chemotherapy would be more/less beneficial undermined equipoise and made it difficult for patients to engage with OPTIMA Prelim. To tackle these challenges, individual and group recruiter feedback focussed on communication issues and vignettes of eligible patients were discussed to address discomforts around approaching patients. 'Tips' documents concerning structuring discussions and conveying equipoise were disseminated across sites, together with revisions to the Patient Information Sheet. CONCLUSIONS: This is the first study illuminating the tension between oncologists' routine practices and recruitment to de-escalation trials. Although time and resources are required, these challenges can be addressed through specific feedback and training as the trial is underway.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".