The Deferred Consent Model in a Prospective Observational Study Evaluating Myocardial Injury in the Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Informed consent is a hallmark of ethical clinical research. An inherent challenge in critical care research is obtaining consent when patients lack decision-making capacity. One solution is deferred consent, which is often used for studies that are low risk or involve emergency interventions. Our objective was to describe a deferred consent model in a low-risk critical care study. METHODS: Prognostic Value of Elevated Troponins in Critical Illness Study was a prospective, pilot observational study of critically ill patients in 3 intensive care units, involving serial electrocardiograms and cardiac biomarkers. Newly admitted patients were enrolled over 1 month. When possible, informed consent was obtained a priori from the patient or substitute decision maker (SDM); otherwise, consent was deferred until the patient regained consent capacity or until their SDM was available. Logistic regression analysis was used to determine the association between patient's sex, Acute Physiology and Chronic Health Evaluation II score, study center, person providing consent (patient vs SDM), method of consent (telephone vs in person), and the provision or not of informed consent. RESULTS: The overall consent rate was 80.1% (213 of 266 persons approached). Of the 53 persons declining consent, 37 (69.8%) agreed to the use of data collected up until that point. Over half of all consent encounters were with patients rather than SDMs. Median interval delay between enrollment and the consent encounter was 1 day. On multivariate analysis, the only variable associated with consent was male sex of the patient (odds ratio for males 2.59, confidence interval: 1.19-5.63). CONCLUSION: Deferred consent facilitates implementation of time-sensitive research protocols until a consent encounter is possible. As a feasible alternative to exclusive a priori consent, the deferred consent model can be useful in low-risk studies in critically ill patients.
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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 | 0.073 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".