Research Recruitment Practices and Critically Ill Patients. A Multicenter, Cross-Sectional Study (The Consent Study)
Bibliographic record
Abstract
RATIONALE: Limited cross-sectional data exist to characterize the challenges of enrolling critically ill patients into research studies. OBJECTIVES: We aimed to describe recruitment practices, document factors that impact recruitment, and identify factors that may enhance future research feasibility. METHODS: We conducted a prospective, observational study of all critically ill adults eligible to participate in research studies at 23 Canadian intensive care units. We characterized eligibility events into one of five consent outcomes, identified reasons why opportunities to recruit were missed or infeasible, and documented decision maker's rationale for providing or declining consent. MEASUREMENTS AND MAIN RESULTS: Patients made decisions for themselves in 8.9% of encounters. In 452 eligibility events, consent was not required in 14 (3.1%), missed in 130 (28.8%), infeasible due to operational reasons in 129 (28.5%), obtained in 140 (31.0%), and declined in 39 (8.6%). More than half (57.3%) of all opportunities to recruit patients were missed or infeasible, largely because of research team workload, limited availability, narrow time windows for inclusion, difficulties in contacting families, nonexistent substitute decision makers (SDMs), physician refusals, and protocols prohibiting coenrollment. The rationale for providing consent differed between patients and SDMs. Greater research coordinator experience and site research volume and broader time windows for inclusion were significant predictors of fewer declined consents. CONCLUSIONS: A large gap exists between eligibility and the frequency with which consent encounters occur in intensive care unit research. Recruitment is susceptible to design and procedural inefficiencies that hinder recruitment and to personnel availability, given the need to interact with SDMs. Current enrollment practices may underrepresent potential study populations.
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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 | 0.051 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| 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".