Public perceptions of participation in trials in the intensive care unit: an ERS Clinical Research Collaboration international cross-sectional survey across 13 countries
Bibliographic record
Abstract
Background: Public views of trial participation in the intensive care unit (ICU) are often not sought, yet as patients and study participants, they are profoundly affected. We aimed to understand public opinions on pandemic and non-pandemic ICU trials, consent, adaptive trials and personalised medicine, following the COVID-19 pandemic. Methods: Views of nationally representative samples were collected through a cross-sectional scenario-based survey in 13 countries; Australia, Canada, France, Germany, Ghana, India, Ireland, Italy, Japan, New Zealand, Pakistan, UK and USA and analysed by descriptive and regression analyses. Results: Of 9726 respondents, 7382 (75.9%) felt pandemic research is important, that trials should be established in interpandemic periods (76.1%) with special rules to make it quicker/easier (72.8%) and patients and public involved (73%). Less respondents indicated trust in their government’s pandemic management (45.3%). A minority of respondents indicated they would not want to participate in low-risk (1627,16.7%) and higher-risk (1823,18.7%) pandemic ICU trials. Respondents supported alternative consent approaches to prospective consent (79.8%, e.g. deferred/substitute decision maker). Few respondents were not supportive of biological sampling (11.8%), adaptive trials (5.3%) and personalised medicine (6%). In 9 of 13 countries asked about non-pandemic respiratory ICU trials, only 17.3% of respondents would not want to participate. Previous ICU and pandemic experience and trust were predictive of agreement to participate. Conclusions: Our study highlights public support for pandemic and non-pandemic ICU trials. It is essential to understand such views to inform trials, build trust and understanding. This work has informed PANTHER and REMAP-CAP platform trials.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".