Informed Consent in an International Trial of Stress Ulcer Prophylaxis - Patterns and Predictors: A Protocol and Statistical Analysis Plan
Bibliographic record
Abstract
Abstract Background Informed consent rates are inconsistently incorporated in trial reports, and literature on consent patterns and predictions is sparse, particularly in the field of critical care. Objective The overall objective of this study is to describe the patterns and predictors of consent rates in REVISE. The specific aims are to analyze the consent models used, consent rates, participants in the consent encounter, reasons for declined consent, and factors associated with obtaining consent. Methods This is a pre-planned secondary study of the REVISE Trial ( NCT03374800 ) which compared pantoprazole to placebo on the outcome of clinically important upper gastrointestinal bleeding among invasively ventilated patients in the intensive care unit (ICU). Research ethics committees approved the protocol in all jurisdictions. Research personnel prospectively collected standardized data for each consent encounter, including the consent model ( a priori , deferred, or opt-out), the role of the individual who provided or declined consent (patient, SDM, other), and the reasons for declined consent, the role of the individual who requested consent (research coordinator, site investigator, ICU physician) and the consent encounter method (in person or via telephone). When consent was provided and then later revoked, who revoked consent (patient, SDM, other) and timing (in ICU, in hospital, post hospital), as well as details about permission for data retention were collected. In this study, consent rates will be calculated across REVISE sites, and in relation to the COVID-19 pandemic. We will also calculate the consent rates for a priori and deferred consent models, the timing from deferred recruitment to consent provided or declined, which personnel requested consent (research coordinator, site investigator, ICU physicians, other), and who engaged in the consent encounter for each consent model (patient, SDM, other). Multilevel logistic regression analysis will be conducted to evaluate variables independently associated with consent including additional site and staff variables. Results By analyzing the frequency and impact of consent models, and consent encounters with various stakeholders, results will highlight the acceptability of different approaches, and the impact of different approaches in this critical care trial. Conclusions This pre-planned retrospective sub-study using an international randomized controlled trial database will provide useful informed consent metrics and knowledge that is relevant to contemporary global trial conduct.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.200 | 0.249 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.035 | 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".