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Record W3128056861 · doi:10.21203/rs.3.rs-147007/v1

Pre-hospital Transdermal Glyceryl Trinitrate in Patients With Stroke Mimics: Data From the Right-2 Randomised-controlled Ambulance Trial

2021· preprint· en· W3128056861 on OpenAlexafffund
Bronwyn Tunnage, Lisa J Woodhouse, Mark Dixon, Sandeep Ankolekar, Jason P. Appleton, Lesley Cala, Timothy J. England, Kailash Krishnan, Diane Havard, Grant Mair, Keith W. Muir, Steve Phillips, John F. Potter, Christopher Price, Marc Randall, Thompson Robinson, Christine Roffe, Else Charlotte Sandset, A Niroshan Siriwardena, Polly Scutt, Joanna M. Wardlaw, Nikola Sprigg, Philip M. Bath

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsDalhousie University
FundersNIHR Nottingham Biomedical Research CentrePeking University Health Science CenterUK Dementia Research InstituteStiftelsen Norsk LuftambulanseFaculty of Health and Medical Sciences, University of Western AustraliaNIHR Leicester Biomedical Research CentreKeele UniversityPeking UniversityDalhousie UniversityUniversity of GlasgowNewcastle UniversityAuckland University of Technology, New ZealandKing's College LondonUniversity of LeicesterUniversity of NottinghamUniversity of East AngliaBritish Heart FoundationNational Institute for Health and Care ResearchUniversity of New South WalesNottingham University Hospitals NHS TrustUniversity Hospitals Birmingham NHS Foundation TrustKing's College Hospital NHS Foundation Trust
KeywordsMedicineTransdermalStroke (engine)Randomized controlled trialAnesthesiaEmergency medicineSurgeryPharmacologyEngineering

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.037
GPT teacher head0.339
Teacher spread0.303 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
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

Citations1
Published2021
Admission routes2
Has abstractno

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