Advance Consent for participation in Acute Stroke Trials (ACTION): protocol for a feasibility study
Bibliographic record
Abstract
INTRODUCTION: Obtaining informed consent for research from patients in medical emergencies remains a challenge, particularly in acute stroke care as treatment must be administered quickly and patients often arrive in the hospital in a state of incapacitation. Adaptations to standard consenting approaches-such as the use of surrogate consent or deferral of consent-have significant limitations. This feasibility study aims to test a new consenting approach in acute stroke care that we call advance consent. Advance consent has the potential to render emergency trial enrolment faster, fairer and more transparent, leading to more generalisable results. METHODS AND DESIGN: We will conduct a five-part study at The Ottawa Hospital, a quaternary care stroke centre: (1) administering questionnaires in the Ottawa Hospital Stroke Prevention Clinic that will examine patients' perspectives on research participation and advance consent; (2) inviting participants to consent in advance to any or both currently enrolling acute stroke trials; (3) tracking patient enrolment into these trials over 1 year; (4) administering a follow up questionnaire to participants at 1 year and (5) administering a questionnaire to participating hospital staff in order to interrogate their experiences with advance consent. Outcomes include but are not limited to eligibility rate, recruitment rate, withdrawal rate and the proportion of patients whose advance consent results in trial enrolment. CONCLUSION: This study will test the feasibility of enrolling patients at risk of stroke into acute stroke trials using advance consent.
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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 | 0.190 | 0.161 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.102 | 0.028 |
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".