A Randomized Controlled Trial of Behavioral Nudges to Improve Enrollment in Critical Care Trials
Bibliographic record
Abstract
Abstract Rationale Low and slow patient enrollment remains a barrier to critical care randomized controlled trials (RCTs). Behavioral economic insights suggest that nudges may address some enrollment challenges. Objectives To evaluate the efficacy of a novel preconsent survey consisting of nudges on critical care RCT enrollment. Methods We conducted an RCT in 10 intensive care units (ICUs) among surrogate decision-makers (SDMs). The novel multicomponent behavioral nudge survey was administered immediately before soliciting SDMs’ informed consent for their patients’ participation in a sham trial of two mechanical ventilation weaning approaches in acute respiratory failure. The primary outcome was the enrollment rate for the sham trial. Secondary outcomes included undue and unjust inducements. We also explored SDM and patient predictors of enrollment using multivariate regression. Results Among 182 SDMs, 93 were randomized to receive the intervention survey and 89 to receive standard informed consent. There was no statistically significant difference in enrollment rates between the intervention (29%) and standard consent (34%) groups (percentage difference, 5%; 95% confidence interval [CI], −9% to 18%; P = 0.50). There was no evidence of undue or unjust inducement. White SDMs were more likely to enroll the patient compared with non-white SDMs (odds ratio, 3.7; 95% CI, 1.1 to 12.2; P = 0.03). SDMs who perceived a higher risk of participation were less likely to enroll the patient (odds ratio, 0.57; 95% CI, 0.46 to 0.71; P < 0.001). Conclusions A preconsent behavioral nudge survey among SDMs of patients with acute respiratory failure in the ICU did not increase enrollment rates for a sham RCT compared with standard informed consent procedures. Clinical trial registered with ClinicalTrials.gov (NCT03284359).
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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 | 0.064 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".