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Record W4406962851 · doi:10.1161/str.56.suppl_1.tp119

Abstract TP119: What do participants want in a consent form? A systematic review

2025· review· en· W4406962851 on OpenAlexaff
Rena Seeger, Katerina Palacek, Emma Cummings, Brian Dewar, Michel Shamy

Bibliographic record

VenueStroke · 2025
Typereview
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsVanier CollegeUniversity of Ottawa
Fundersnot available
KeywordsMedicineInformed consentFamily medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

Introduction: Informed consent is a cornerstone of modern clinical research, though ensuring consent has long been recognized as problematic. This is especially true for acute stroke trials, where the time-sensitive nature of treatment challenges traditional consent practices. The era of platform adaptive trials will likely add further complexity to acute stroke consent. Therefore, we sought to survey the literature for any empirical studies documenting research participants’ preferences around what content is most important to include in consent forms. Methods: We conducted a systematic review of the literature to identify empirical studies reporting patients’ opinions about what content is most important to include in consent forms for research participation. Eligible studies included surveys, focus groups, or interviews. Based on a review of consent form templates, we identified 18 elements that commonly appear in consent forms, and used these to guide our extraction of potential content. Results: Of the 1,444 studies screened by title and abstract, 35 were sent to full text review, and data were extracted from 17 studies. To determine the ubiquity of the importance of these topics, we counted if each of these topics appeared in the 17 papers. The most commonly mentioned items included risks (65%, 11/17 studies), potential benefits (53%, 9/17 studies), study procedures e.g. blood draws and imaging (47%, 8/17 studies), confidentiality (47%, 8/17 studies), study rationale (41%, 7/17 studies), and voluntariness (41%, 7/17 studies). The least important elements included information about the condition and investigator conflicts of interest. Conclusion: In the era of adaptive platform trials for acute stroke, there is the potential for consent forms to become incredibly lengthy and complex. This is the first systematic review of which we are aware that seeks to identify what information is most important to be included in consent forms to participate in research studies. Unfortunately, data are limited but they suggest that consent form optimization is possible. Only risks and potential benefits were identified as important in a majority of studies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.181
GPT teacher head0.425
Teacher spread0.244 · 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; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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