Who says “no” to participating in stroke clinical trials and why: an observational study from the Vancouver Stroke Program
Bibliographic record
Abstract
BACKGROUND: Successful stroke trials require adequate recruitment. In this observational study, we assessed reasons for refusal to provide informed consent in eligible patients approached for clinical trial participation at the Vancouver Stroke Program. METHODS: We assessed screening logs from four trials that were actively recruiting at our center: three randomized trials, two of which investigated different antithrombotic strategies for secondary prevention (NAVIGATE-ESUS, NCT02313909 12/2014; DATAS-II, NCT02295826 11/2014) and one that investigated surgery plus medical management versus medical management alone for primary prevention (CREST-2, NCT02089217 03/2014). The fourth study was observational and non-randomized; all participants received an external monitoring device (PROPHECY, NCT03712865 10/2018). Screening logs from June 2015 to April 2017 were reviewed retrospectively. Subsequently, we used a prospective structured case report form for screening (May 2017-March 2018). We assessed and compared refusal rates between trials, demographics of those refusing consent, and their reasons for doing so. We used descriptive statistics, chi-square and Fisher's exact tests as appropriate for non-parametric data, and t-tests for parametric data. We examined likelihood of refusal by sex using multivariable logistic regression models including age and trial intervention as co-variables. RESULTS: A total of 235 patients (43% women) were approached for consent. More patients refused the surgical (59%) and antithrombotic trials (53%) compared with the non-randomized external monitoring device study (13%) (p < 0.001). Surgical trial refusals were primarily due to a desire for certainty in receiving a particular intervention (39%), with the majority of those patients wanting surgery. Refusals for the antithrombotic trials were mainly due to concerns with the potential side effects of the study drug (41%); refusals in the device trial were mainly due to disinterest (46%). Women refused participation more often than men (48% vs 33%). Women remained less likely to consent than men, even after adjustment for age and trial intervention (OR 0.46, 95% CI 0.26-0.82, p = 0.009). CONCLUSIONS: Concern surrounding drug safety, randomization, and disinterest were the chief deterrents to enrolment; there were also differences in rates of consent by gender. A better understanding of why patients refuse participation in stroke trials may help to develop future patient-directed communication strategies to improve enrolment. Further research is required to better understand the reasons underlying gender disparities in consent rates.
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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 | 0.169 | 0.437 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".