Community Consultation for Planned Emergent Use Research: Experiences From an Academic Medical Center
Bibliographic record
Abstract
BACKGROUND: Emergent use research-research involving human subjects that have a life-threatening medical condition and who are unlikely to provide informed consent-in critical illness is fraught with challenges related to obtaining informed consent. Per federal regulations, to meet criteria to conduct such trials, the investigators have to seek community consultations. Effective ways of obtaining this consultation remains ill-defined. OBJECTIVE: We sought to describe methods, interpretations, and our experiences of conducting community consultation in a planned emergent use randomized controlled trial. METHODS: As part of a planned emergent use clinical trial in our study, community consultation consisted of four focus groups sessions with members from the community in which the clinical trial was conducted. Three focus group sessions were conducted with members who had an affiliation to Mayo Clinic, and the other focus group session was conducted with non-Mayo affiliation members. The feedback from the focus group sessions led to the creation of the public notification plan. The public was notified of the trial through community meetings as well as social media. RESULTS: As compared to community meetings, focus group sessions resulted in greater attendance with more interactive discussions. Moreover, focus group sessions resulted in greater in-depth conversations leading to institutional acceptance of the clinical trial under study. CONCLUSIONS: Exception from informed consent can be acceptable to the community. Focus groups provided better participation and valuable interactive insight as compared to community meetings in our study. This could serve as a valuable guide for investigators pursuing exception from informed consent in their research studies.
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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 | 0.038 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| 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".