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Record W2913327195 · doi:10.3899/jrheum.181096

OMERACT Filter 2.1: Elaboration of the Conceptual Framework for Outcome Measurement in Health Intervention Studies

2019· article· en· W2913327195 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of Ottawa
FundersLeeds Biomedical Research CentrePfizer AustraliaU.S. Department of Veterans AffairsNational Institute for Health and Care ResearchAgence Nationale de la RecherchePfizer
KeywordsFilter (signal processing)Outcome (game theory)Set (abstract data type)Core (optical fiber)Intervention (counseling)Conceptual frameworkComputer scienceProcess (computing)PsychologyProcess managementMedicineMathematicsEngineeringSociologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Filter 2.0 framework was developed in 2014 to aid core outcome set development by describing the full universe of "measurable aspects of health conditions" from which core domains can be selected. This paper provides elaborations and updated concepts (OMERACT Filter 2.1). METHODS: At OMERACT 2018, we discussed challenges in the framework application caused by unclear or ambiguous wording and terms and incompletely developed concepts. RESULTS: The updated OMERACT Filter 2.1 framework makes benefits and harms explicit, clarifies concepts, and improves naming of various terms. CONCLUSION: We expect that the Filter 2.1 framework will improve the process of core set development.

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.

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)0.012
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.254
GPT teacher head0.507
Teacher spread0.253 · 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