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Record W2913497291 · doi:10.3899/jrheum.181185

Patient Perspectives on DMARD Safety Concerns in Rheumatology Trials: Results from Inflammatory Arthritis Patient Focus Groups and OMERACT Attendees Discussion

2019· article· en· W2913497291 on OpenAlexaffvenue
Kathleen M. Andersen, Ayano Kelly, Anne Lyddiatt, Clifton O. Bingham, Vivian P. Bykerk, Adena Batterman, Joan Westreich, M.K. Jones, Marita Cross, Lyn March, Beverley Shea, Peter Tugwell, Lee S. Simon, Robin Christensen, Susan J. Bartlett

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsOttawa Hospital
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthOak Foundation
KeywordsMedicineRheumatologyInternal medicineArthritisPhysical therapyInflammatory arthritisAlternative medicineClinical trialFamily medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Safety Working Group is identifying core safety domains that matter most to patients with rheumatic disease. METHODS: International focus groups were held with 39 patients with inflammatory arthritis to identify disease-modifying antirheumatic drug (DMARD) experiences and concerns. Themes were identified by pragmatic thematic coding and discussed in small groups by meeting attendees. RESULTS: Patients view DMARD side effects as a continuum and consider the cumulative effect on day-to-day function. Disease and drug experiences, personal factors, and life circumstances influence tolerance of side effects and treatment persistence. CONCLUSION: Patients weigh overall adverse effects and benefits over time in relation to experiences and life circumstances.

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)0.053
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.097
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.279
Teacher spread0.263 · 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 designQualitative
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

Citations14
Published2019
Admission routes2
Has abstractyes

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