Enhancing the informed consent process for critical care research: Strategies from a thromboprophylaxis trial
Bibliographic record
Abstract
BACKGROUND: Critically ill patients lack capacity for decisions about research participation. Consent to enrol these patients in studies is typically obtained from substitute decision-makers. OBJECTIVE: To present strategies that may optimise the process of obtaining informed consent from substitute decision-makers for participation of critically ill patients in trials. We use examples from a randomised trial of heparin thromboprophylaxis in the intensive care unit (PROTECT, clinicaltrials.gov NCT00182143). METHODS: 3764 patients were randomised, with an informed consent rate of 82%; 90% of consents were obtained from substitute decision-makers. North American PROTECT research coordinators attended three meetings to discuss enrolment: (1) Trial start-up (January 2006); (2) Near trial closure (January 2010); and (3) Post-publication (April 2011). Data were derived from slide presentations, field notes from break-out groups and plenary discussions, then analysed inductively. RESULTS: We derived three phases for the informed consent process: (1) Preparation for the Consent Encounter; (2) The Consent Encounter; and (3) Follow-up to the Consent Encounter. Specific strategies emerged for each phase: Phase 1 (four strategies); Phase 2 (six strategies); and Phase 3 (three strategies). CONCLUSION: We identified 13 strategies that may improve the process of obtaining informed consent from substitute decision-makers and be generalisable to other settings and 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 | 0.732 | 0.713 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".