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Record W4321168231 · doi:10.1017/cjn.2023.12

Advance Consent in Acute Stroke Trials: Survey of Canadian Stroke Physicians

2023· article· en· W4321168231 on OpenAlexafffundvenueabout
Ubong Udoh, Brian Dewar, Stuart G. Nicholls, Mark Fedyk, Robert Fahed, Jeffrey J. Perry, Michael D. Hill, Bijoy K. Menon, Richard H. Swartz, Alexandre Y. Poppe, Sophia Gocan, Jamie Brehaut, Katie N. Dainty, Victoria Shepherd, Dar Dowlatshahi, Michel Shamy

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2023
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of TorontoNorth York General HospitalSunnybrook Health Science CentreCentre Hospitalier de l’Université de MontréalUniversity of CalgaryHealth Sciences CentreOttawa Hospital
FundersCanadian Institutes of Health ResearchCanadian Stroke ConsortiumGovernment of CanadaHealth and Care Research WalesFondation Brain CanadaStrykerSun PharmaBiogenAlberta InnovatesHeart and Stroke Foundation of Canada
KeywordsAcute strokeInformed consentStroke (engine)MedicineClinical trialFamily medicinePhysical therapyMedical emergencyAlternative medicineEmergency departmentPsychiatryInternal medicinePathology

Abstract

fetched live from OpenAlex

Advance consent presents a potential solution to the challenge of obtaining informed consent for participation in acute stroke trials. Clinicians in stroke prevention clinics are uniquely positioned to identify and seek consent from potential stroke trial participants. To assess the acceptability of advance consent to Canadian stroke clinic physicians, we performed an online survey. We obtained 58 respondents (response rate 35%): the vast majority (82%) expressed comfort with obtaining advance consent and 92% felt that doing so would not be a significant disruption to clinic workflow. These results support further study of advance consent for acute stroke trials.

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)0.020
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.410

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.492
GPT teacher head0.508
Teacher spread0.017 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

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

Citations7
Published2023
Admission routes4
Has abstractyes

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