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Record W2070764609 · doi:10.3899/jrheum.131314

How to Choose Core Outcome Measurement Sets for Clinical Trials: OMERACT 11 Approves Filter 2.0

2014· article· en· W2070764609 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Journal of Rheumatology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFace validityObservational studySet (abstract data type)Core (optical fiber)Outcome (game theory)Filter (signal processing)Process (computing)Medical educationComputer scienceInternal medicinePsychometricsClinical psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) initiative works to develop core sets of outcome measures for trials and observational studies in rheumatology. At the OMERACT 11 meeting, substantial time was devoted to discussing a conceptual framework and a proposal for a more explicit working process to develop what we now propose to term core outcome measurement sets, collectively termed "OMERACT Filter 2.0." METHODS: Preconference work included a literature review, and discussion of preliminary proposals through face-to-face discussions and Internet-based surveys with people within and outside rheumatology. At the conference, 5 interactive sessions comprising plenary and small-group discussions reflected on the proposals from the viewpoint of previous and ongoing OMERACT work. These considerations were brought together in a final OMERACT presentation seeking consensus agreement for the Filter 2.0 framework. RESULTS: After debate, clarification, and agreed alterations, the final proposal suggested all core sets should contain at least 1 measurement instrument from 3 Core Areas: Death, Life Impact, and Pathophysiological Manifestations, and preferably 1 from the area Resource Use. The process of core set development for a health condition starts by selecting core domains within the areas ("core domain set"). This requires literature searches, involvement (especially of patients), and at least 1 consensus process. Next, developers select at least 1 applicable measurement instrument for each core domain. Applicability refers to the original OMERACT Filter and means that the instrument must be truthful (face, content, and construct validity), discriminative (between situations of interest) and feasible (understandable and with acceptable time and monetary costs). Depending on the quality of the instruments, participants formulate either a preliminary or a final "core outcome measurement set." At final vote, 96% of participants agreed "The proposed overall framework for Filter 2.0 is a suitable basis on which to elaborate a Filter 2.0 Handbook." CONCLUSION: Within OMERACT, Filter 2.0 has made established working processes more explicit and includes a broadly endorsed conceptual framework for core outcome measurement 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)0.094
metaresearch head score (Gemma)0.083
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0940.083
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.683
GPT teacher head0.591
Teacher spread0.092 · 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