Deferred Consent in a Minimal‐Risk Study Involving Critically Ill Subarachnoid Hemorrhage Patients
Bibliographic record
Abstract
INTRODUCTION: Alterations from first-party and surrogate decision-maker consent can enhance the feasibility of research involving critically ill patients. OBJECTIVE: To describe the use of a deferred-consent model to enable participation of critically ill patients in a minimal-risk biomarker study. METHODS: A prospective observational study was conducted in which serum biomarker samples were collected three times daily over the first 14 days following aneurysmal subarachnoid hemorrhage. Sample collection was initiated on intensive care unit admission and consent was obtained when research personnel could approach the patient or the patient's surrogate decision maker. RESULTS: Twenty-seven patients were eligible for the study, of whom only five were capable of providing informed consent. Full consent was obtained for 21 (78%) patients through self- (n=4) and surrogate (n=17) consent. Partial consent or refusal (only permitting the collection of blood samples as a part of routine care or use of data) occurred in three patients. Among the 22 consents sought from surrogates, three (11%) refused participation. The refusals included the sickest patients in the cohort. Once consent was provided, no patient or surrogate withdrew consent before study completion. DISCUSSION: Use of a deferred consent model enabled participation of critically ill patients in a minimal-risk biomarker study with no withdrawals. CONCLUSIONS: Further research and enhanced awareness of the potential utility of hybrid models, including deferred consent in addition to patient or surrogate consent, in the conduct of low-risk and minimally interventional time-sensitive studies of critically ill patients are required.
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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 | 0.079 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".