Impact of Adherence to Golimumab on Disease Flares in Rheumatoid Arthritis: Results from a Canadian Observational Study
Bibliographic record
Abstract
Louis Bessette,1 Pauline Boulos,2 Regan Arendse,3 Proton Rahman,4 Sam Aseer,4 Thanu Ruban,5,6 Meagan Rachich,7 Francois Nantel,8 Adriana Calce,7 Odalis Asin-Milan,7 Derek Haaland2,9,10 1Department of Medicine, Laval University, Québec, QC, Canada; 2Department of Medicine, McMaster University, Hamilton, ON, Canada; 3College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada; 4Faculty of Medicine, Division of Rheumatology, Memorial University of Newfoundland, St. John’s, NL, Canada; 5Division of Rheumatology, Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; 6Markham Rheumatology Centre, Markham, ON, Canada; 7Johnson & Johnson, Toronto, ON, Canada; 8Nantel MedSci Consult, Montréal, QC, Canada; 9The Waterside Clinic, Orillia, ON, Canada; 10Department of Medicine, Northern Ontario School of Medicine University, Sudbury, ON, CanadaCorrespondence: Derek Haaland, The Waterside Clinic, 79 Colborne Street East, Orillia, ON, L3V 1T6, Canada, Tel +1-705-734-3943, Fax +1-705-734-0007, Email derekhaaland@thewatersideclinic.caObjective: To assess the association between adherence to golimumab treatment and the incidence of disease flares in patients with rheumatoid arthritis (RA) in routine clinical practice.Methods: A 12-month (M) prospective observational study conducted across 27 Canadian centers, involving patients with RA receiving golimumab as part of routine clinical care. Treatment adherence was assessed with the Compliance Questionnaire in Rheumatology (CQR); non-adherence was defined as a weighted baseline score predictive of ≤ 80% compliance. Secondary definitions involved the CQR score at M6 and M12. Disease flaring was assessed with the RA-Flare Questionnaire (RA-FQ); flare was defined as a positive response to question 7 (“Are you having a flare?”). The association between adherence and disease flares was analyzed by comparing RA-FQ scores and the proportion of patients reporting flares between the high and low adherence groups. The association between adherence and glucocorticoid use or adverse event (AE) incidence was similarly assessed.Results: Of 215 patients enrolled, 169 (78.6%) completed the study. No significant difference in mean RA-FQ scores was observed between low and high adherence groups at M6 (22.5 vs 23.8; p=0.56) and M12 (20.8 vs 19.9; p=0.70); disease flares were reported by 35.7% of low adherence patients, compared to 28.2% in the high adherence group (p=0.34). At M12, these rates were 30% vs 24.7%, respectively (p=0.49). Glucocorticoid use was comparable between baseline adherence groups, although a higher rate was observed in the low visit-predicted adherence group based on the M6 CQR score (30.5% vs 16.3%; p=0.04). No significant differences were observed in AE incidence.Conclusion: In this study, no significant differences in RA-FQ scores and the proportions of patients reporting disease flares or AEs were observed between patients with RA with low and high predicted adherence to golimumab. The increased glucocorticoid use in patients with low adherence merits further investigation.Trial Registration: ClinicalTrials.gov identifier, NCT03729349.Keywords: biological disease-modifying antirheumatic drugs, TNF inhibitor, adherence, rheumatoid arthritis, flares
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".