Informed Consent Documents Used in Critical Care Trials Often Do Not Implement Recommendations*
Bibliographic record
Abstract
OBJECTIVE: Informed consent documents are often poorly understood by research participants. In critical care, issues such as time pressure, patient capacity, and surrogate decision making complicate the consent process further. Recommendations exist for addressing critical care-specific consent issues; we examined how well existing practice implements these recommendations. DESIGN: We conducted a systematic search of the literature for recommendations specific to critical care informed consent and rated existing informed consent documents on their implementation of 1) 18 of these critical care recommendations and 2) 36 previously developed general informed consent recommendations. Four hundred twelve registered critical care trials were identified and a request sent to the principal investigators for an example of the informed consent document associated with the trial. Each consent document was rated on both set of recommendations. SETTING: We evaluated informed consent documents for trials conducted in English or French registered with clinicaltrials.gov. PATIENTS: Not applicable. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Independent coders rated implementation of each recommendation on a four-point scale. Of 412 requests, 137 informed consent documents were returned, for a response rate of 34.1%. Of these, 86 met inclusion criteria and were assessed. Overall agreement between raters was 90.6% (weighted κ = 0.79; 0.77-0.81). Implementation of the 18 critical care recommendations was highly variable, ranging between 2% and 96.5%. CONCLUSIONS: Critical care studies often do not provide the information recommended for those providing consent for research. These clear recommendations provide testable hypotheses about how to improve the consent process for patients and family members considering trial participation in the critical care setting.
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.
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 | 0.792 | 0.915 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".