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Optimizing the Performance of the Surrogate Informed Consent Process for Critical Care Research

2025· article· en· W4410276220 on OpenAlexaff
Rafaela Avallone Mantelli, C. Glaros, Caroline Tietbohl, KRISTEN A. TORRES, D. Clark Files, Matthew F. Mart, Michael A. Matthay, Karen E. A. Burns, Daniel D. Matlock, Matthew K. Wynia, Mark Moss

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineInformed consentSurrogate endpointMEDLINECritical illnessProcess (computing)Intensive care medicineAlternative medicineInternal medicineCritically illPathology

Abstract

fetched live from OpenAlex

Abstract RATIONALE: Research in intensive care units (ICUs) is essential to improving care for critically ill patients; however, patients are often unable to consent for themselves. Surrogates are often required to participate in the informed consent process for critical care research, though how to best engage surrogates in this process remains unclear. This study seeks to identify best practices for conducting surrogate consent for critical care research. METHODS: We conducted a mixed-methods study including quantitative surveys with open-ended questions, focus groups, and semi-structured interviews with principal investigators (PIs), research coordinators (RCs), surrogate decision makers who had been approached about a critical care research clinical trial, and when possible, the patient who had been critically ill. RESULTS: In total, 230 individuals (105 RC, 90 PI, 27 surrogates, 8 patients) completed surveys, and 61 participated in focus groups or interviews. In both surveys and focus groups/interviews, participants across all groups believed that RCs (as opposed to PIs) should conduct the consent process, as RCs are not considered to be authority figures and have fewer perceived conflicts of interest that could influence surrogates decision-making. Surrogates appreciated it when research staff waited until an optimal time to initiate contact and were given physical space and a defined period to consider their decision before follow up with them. When compared to PI/RCs, surrogates and/or patients attributed more importance to seeing the research team as an additional resource in explaining the progress of the patient's care and were appreciated having additional team members whom they perceived as advocating for adherence to clinical protocols for their loved ones (p<0.0001 and p=0.0016). Compared to PI/RCs, surrogates thought the written consent was more important and were less concerned with its length, (p=0.001 and p<0.0001). In general, all participants felt that phone and electronic consent was less effective than in-person consent, though these modalities could facilitate the process for distant surrogates. Consent timing, respect for surrogate decision-making autonomy, and clear communication of the patient's presumed wishes were additional significant themes. CONCLUSIONS: Our study highlights the need for better guidance for carrying out the surrogate consent process in ICU research and identifies several themes that could serve to develop recommendations including designating trained RCs as primary facilitators, improving consent timing and setting, and for implementing accessible consent documentation. This study supports developing standardized training and guidelines for the surrogate informed consent process that could be consistently applied by ethics review boards in reviewing consent processes for clinical care research.

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 imitation

Not 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.

metaresearch head score (Codex)0.805
metaresearch head score (Gemma)0.832
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.992
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8050.832
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0080.007
Science and technology studies0.0060.015
Scholarly communication0.0140.017
Open science0.0070.014
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0080.005

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.

Opus teacher head0.343
GPT teacher head0.598
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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