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Record W2175940543 · doi:10.1016/j.ejvs.2015.08.014

Transatlantic Multispecialty Consensus on Fundamental Endovascular Skills: Results of a Delphi Consensus Study

2015· article· en· W2175940543 on OpenAlexaff
Heidi Maertens, Rajesh Aggarwal, Sumaira Macdonald, Frank Vermassen, Isabelle Van Herzeele, Marianne Brodmann, Jocie DeVries, Roberto Ferraresi, Gary M. Ansel, Peter Goverde, William A. Gray, Johannes Lämmer, J. Lee, Geert Maleux, C Metzger, E Minar, Robert Morgan, Jihad Mustapha, Sonia Ronchey, John H. Rundback, Marc L. Schermerhorn, Darren B. Schneider, Patricia Schneider, Jörg Teßarek, Ignace F.J. Tielliu, J. van den Berg, Fabio Verzini, Mark H. Wholey

Bibliographic record

VenueEuropean Journal of Vascular and Endovascular Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineConsensus conferenceDelphiDelphi methodMEDLINEMedical physicsInternal medicineStatisticsLaw

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)0.203
metaresearch head score (Gemma)0.206
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2030.206
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.003
Science and technology studies0.0040.003
Scholarly communication0.0030.004
Open science0.0020.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.063
GPT teacher head0.286
Teacher spread0.223 · 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 designQualitative
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

Citations22
Published2015
Admission routes1
Has abstractno

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