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315. Development of an ANCA-Associated Vasculitis Patient-Reported Outcome Measure: Identification of Salient Themes and Candidate Questionnaire Item Development

2015· article· en· W2341153011 on OpenAlexaffabout
Joanna Robson, Jill Dawson, Nataliya Milman, Katherine Kellom, Peter F. Cronholm, Judy Shea, Raashid Luqmani, Susan P. Ashdown, John T. Farrar, Donald Gebhart, Georgia Lanier, Carol A. McAlear, Jacqueline Peck, Gunnar Tómasson, Peter A. Merkel

Bibliographic record

VenueLara D. Veeken · 2015
Typearticle
Languageen
FieldMedicine
TopicOtitis Media and Relapsing Polychondritis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicinePatient-reported outcomeIdentification (biology)SalientANCA-Associated VasculitisMeasure (data warehouse)VasculitisPhysical therapyPathologyQuality of life (healthcare)Data miningArtificial intelligenceDiseaseNursing

Abstract

fetched live from OpenAlex

Background: Patients with ANCA-associated vasculitides (AAVs), granulomatosis with polyangiitis, eosinophilic granulomatosis with polyangiitis (Churg–Strauss) and microscopic polyangiitis often suffer from persistent disease activity, disease-associated damage, or treatment side effects, all of which may impact quality of life. There is currently no disease-specific patient-reported outcome (PRO) for AAV. The development of a new PRO involves questionnaire item development; item reduction and scale generation; and testing scale properties such as reliability, validity and responsiveness. It is essential that a PRO is developed in compliance with U.S. Food and Drug Administration recommendations in order to legitimize its use in clinical trials and in supporting labelling claims for medications. Following these principles, a multi-national collaboration of researchers and patient-partners has been conducting the first stage of questionnaire item development. A collaborative approach involving patients from the UK, USA and Canada was feasible and desirable due to the relative rarity of the disease and the ability to create a tool with content validity (and cultural/linguistic equivalence) appropriate for use in all three countries.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.030
GPT teacher head0.276
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2015
Admission routes2
Has abstractyes

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