MétaCan
Menu
Back to cohort

The evolution of instrument selection for inclusion in core outcome sets at OMERACT: Filter 2.2

2021· article· en· W3197379691 on OpenAlexaff
Lara Maxwell, Dorcas Beaton, Maarten Boers, Maria Antonietta D’Agostino, Philip G. Conaghan, Shawna Grosskleg, Beverley Shea, Clifton O. Bingham, Annelies Boonen, Robin Christensen, Ernest Choy, Andréa S. Doria, Catherine Hill, Catherine Hofstetter, Féline P B Kroon, Ying Ying Leung, Sarah Mackie, Alexa Meara, Zahi Touma, Peter Tugwell, George A. Wells

Bibliographic record

VenueSeminars in Arthritis and Rheumatism · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsHospital for Sick ChildrenOttawa HospitalCanada Research ChairsInstitute for Work & HealthOttawa Public HealthSickKids FoundationUniversity of TorontoWilfrid Laurier UniversityUniversity of Ottawa
FundersNational Institute for Health and Care ResearchParker Institute for Cancer ImmunotherapyLeeds Biomedical Research CentreAgence Nationale de la RechercheOak Foundation
KeywordsMedicineCore (optical fiber)Selection (genetic algorithm)Outcome (game theory)Inclusion (mineral)Filter (signal processing)Medical physicsArtificial intelligenceMathematical economicsOptics

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)0.181
metaresearch head score (Gemma)0.360
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.959

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1810.360
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0020.001
Scholarly communication0.0060.003
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.068
GPT teacher head0.400
Teacher spread0.332 · 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

Citations61
Published2021
Admission routes1
Has abstractno

Explore more

Same venueSeminars in Arthritis and RheumatismSame topicDelphi Technique in ResearchFrench-language works237,207