Views of consent approaches used in emergency and critical care research: an ERS Clinical Research Collaboration rapid, systematic review of the acceptability of alternative consent models
Bibliographic record
Abstract
Background: The ability to obtain prospective informed consent can be limited in emergency research, including critical care. Opinions of alternative consent models are required and may vary among under-served groups and in the context of the COVID-19 pandemic. Research is needed to inform practice. Methods: We conducted a rapid systematic review of opinions of alternative consent models used in emergency research with searches carried out to July 31 2024. We included quantitative and qualitative studies and investigated under-served groups and pandemic settings. Results: Of 9974 citations, 147 eligible studies were included. Consent models included prospective informed consent (n=28), deferred consent (n=28), surrogate decision maker consent (n=40), healthcare professional consent (n=18) and waived consent (n=45). Groups represented included previous trial participants, relatives, patients, members of the public, healthcare providers, researchers, and ethics committees. In general, alternative consent models were acceptable, with an emphasis on the inclusion of the patient and/or relative in decision-making where possible, and the timing of consent in a high stress setting. Study staff highlighted limitations, such as relative unavailability. Pandemic studies indicated an increased need for alternative consent. Views of under-served groups did not show consensus, however, accommodations to support those groups were largely unreported. Conclusion: Alternative consent models used for emergency research were generally acceptable. This work has informed the consent model in the PANTHER platform trial.
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 | 0.515 | 0.735 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.038 | 0.030 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier 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".