Informed consent in the critically ill: A two-step approach incorporating delirium screening*
Bibliographic record
Abstract
OBJECTIVES: Sedation-agitation and delirium are common in critically ill patients and may be important barriers to informed consent. We describe a two-step process for informed consent and evaluate the natural history of patients' competency by repeated application of this process during their hospitalization. DESIGN: Observational study. SETTING: Nine intensive care units (ICUs) in three teaching hospitals in Baltimore, MD. PATIENTS: One hundred fifty patients with acute lung injury. INTERVENTIONS: Two-step process involving objective evaluation with Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) (step 1), followed by traditional assessment for competency (step 2) in those patients passing step 1. MEASUREMENTS AND MAIN RESULTS: RASS and CAM-ICU assessments (during ICU stay, at consent and hospital discharge); cumulative proportion of patients providing consent at extubation and at ICU and hospital discharge. Of 150 patients, 86 (57%) survived and 77 (90% of survivors) provided consent. Patients were delirious/deeply sedated in 89% of daily assessments during mechanical ventilation. By extubation, 31 (44%) patients passed step 1 and 8 (11%) passed step 2 and were consented. By ICU and hospital discharge, these numbers were 50 (58%) and 18 (21%), and 81 (94%) and 67 (78%), respectively. The median (interquartile range) time to patient consent after acute lung injury diagnosis was 15 (9-28) days. CONCLUSIONS: More than three fourths of critically ill patients are unable to provide informed consent throughout their ICU stay, even after extubation. Sedation-agitation and delirium are common barriers to consent. A two-step consent process, using validated instruments for sedation-agitation and delirium, provides a means of rapidly screening critically ill patients before a more detailed traditional assessment of competency is conducted.
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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 | 0.156 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".