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Record W2913762094 · doi:10.3899/jrheum.181050

Toward a Core Outcome Measurement Set for Polymyalgia Rheumatica: Report from the OMERACT 2018 Special Interest Group

2019· review· en· W2913762094 on OpenAlexaffvenue
Claire Owen, Max Yates, Helen Twohig, Sara Müller, Lorna Neill, Eileen Harrison, Beverley Shea, Lee S. Simon, Catherine Hill, Sarah Mackie

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typereview
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersVersus ArthritisMedical Research CouncilNational Institute for Health and Care Research
KeywordsPolymyalgia rheumaticaRespondentVisual analogue scaleMedicinePhysical therapyRating scaleCore (optical fiber)PsychologyInternal medicineEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: To report the progress of the Outcome Measures in Rheumatology (OMERACT) Polymyalgia Rheumatica (PMR) Working Group in selecting candidate instruments for a core outcome measurement set. METHODS: A systematic literature review identified outcomes measured and instruments used in PMR studies, and a respondent survey and raw data analysis assessed their domain match and feasibility. RESULTS: Candidate instruments were identified for pain [visual analog scale/numerical rating scale (VAS/NRS)], stiffness (VAS/NRS and duration), and physical function (Health Assessment Questionnaire-Disability Index/modified Health Assessment Questionnaire). Domain match and feasibility assessments were favorable; however, validation in PMR was lacking. CONCLUSION: Further assessment of candidate instruments is required prior to recommending a PMR core outcome measurement set.

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.

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)0.231
metaresearch head score (Gemma)0.269
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score0.949

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2310.269
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0090.005
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0030.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.658
GPT teacher head0.526
Teacher spread0.132 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations7
Published2019
Admission routes2
Has abstractyes

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