Impact of Legal Guardian Absence on Research Enrollment in the PICU*
Bibliographic record
Abstract
OBJECTIVES: To identify the frequency of which a legal guardian is at the bedside of children admitted to the PICU that are eligible for research studies. DESIGN: A prospective, observational study. SETTING: Three tertiary Canadian PICUs. PATIENTS: Two hundred one patients were admitted to the PICU between September 2021 and March 2023 (site 1), from March 2019 to March 2020 and March 2022 to March 2023 (site 2), and from March 2019 to March 2020 and July 2020 to November 2021 (site 3). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: At each center, the duration of consent encounters was recorded for patients eligible for research by documenting the length of each attempt (min). The frequency of parental presence at bedside and the ability for a guardian to make a decision were also recorded. Thirty-five percent of patients eligible for research did not have a legal guardian at the bedside on the first attempted consent encounter. Twenty-three percent of approached patients were not enrolled due to an inability for a consent decision to be made by the child's legal guardian or an inability to contact the guardian before discharge. CONCLUSIONS: The absence of legal guardians in the PICU poses a barrier to the enrollment of critically ill children in pertinent research studies and suggests that a model of deferred consent or implied consent would aid in the future of critical care research.
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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 | 0.019 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".