Informed consent in the critically ill: A two-step approach incorporating delirium screening*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it