POS1259 THINK OUTSIDE THE JOINTS: THE IMPACT OF REGIONAL AND WIDESPREAD NON-ARTICULAR PAIN ON SYMPTOMS AND FUNCTION IN THE CANADIAN EARLY ARTHRITIS COHORT
Bibliographic record
Abstract
Background: Pain outside the joint or non-articular pain (NAP) is common and persists in a third of patients with early RA, despite treatment [1]. NAP negatively impacts the ability to reach RA remission [1] but often remains unaddressed in RA care [2]. Little is known about how NAP impacts physical function, social participation and other symptoms important to patients with new-onset RA. Objectives: To evaluate the association of NAP, regional and widespread, on patient-reported outcomes (beyond pain scales) in early RA. Methods: Data were from patients with active early RA (symptoms<1 year, CDAI>2.8) enrolled in the Canadian Early Arthritis Cohort (CATCH) between Jan/2017-10/2023. Patient Reported Outcomes Measurement Information System(PROMIS)29® measures were obtained during regular assessments. Patients were instructed to indicate any non-joint pain they experienced on a Margolis body pain diagram (BPD) at baseline (BL), and 6- and 12-month follow-up visits. Prespecified NAP patterns were classified based on pain reported in 5 sections (4 quadrants and axial, excluding hands and feet) and grouped as: 1) no NAP (no sections selected on BPD), 2) regional (1-3 sections) or 3) widespread NAP(3+sections) [1, 2]. Adjusted associations between repeated measures of NAP and PROMIS-29 Health Domain T-scores were estimated in separate linear-mixed models adjusted for baseline age, sex, education, smoking, comorbidities, osteoarthritis/back pain, seropositivity and lagged (from previous visit) time-variant RA treatment over the first year of follow-up. Results: The study sample included 472 early RA patients; at baseline, 66% were female, mean(sd) age was 57(14); 72% were seropositive, mean symptoms duration 5.2(2.8) months, mean(sd) CDAI 27.0(14.1); most (79%) received a MTX-inclusive regimen. Over half of patients reported NAP at baseline (n=246, 52%); of these with NAP, 72% (176/246) had regional NAP and 28% (70/246) had widespread NAP (Figure 1 shows prevalence in entire cohort). In adjusted regression analyses, compared to no NAP, regional NAP was associated with worse PROMIS29 T-scores (adjusted regression coefficient [95% confidence interval]): physical function -1.7[-2.4, -1.0], social participation -2.6[-3.5, -1.7], pain interference 3.1[2.2, 4.0], sleep disturbance 1.5[0.7, 2.3], fatigue 2.5[1.6, 3.5], anxiety 1.7[0.9, 2.6] and depression 1.6[0.8, 2.5] (Table 1). Compared to those with no NAP, those with widespread NAP reported the largest effects on social participation, pain interference and fatigue (Table 1). Conclusion: Non-articular pain is common and associated with worse symptoms and function throughout the first year of RA. Widespread NAP was associated with significantly greater and clinically meaningful impacts on symptoms and function and had the largest effects on the ability to participate in social roles and activities and on fatigue. Further research is needed to understand how NAP evolves in early RA and to develop targeted interventions for addressing NAP. Figure 1 Table 1 . REFERENCES: [1] Meng C et al. Characterizing NAP at Early RA Diagnosis. Arthritis Rheumatol. 2024 Oct 31. doi: 10.1002/art.43049. PMID: 39482804. [2] Meng C et al. The Association of Patient-Reported NAP with Musculoskeletal Pain Diagnoses and RA Disease Activity [abstract]. Arthritis Rheumatol. 2024; 76 (supp 9) Acknowledgements: NIL . Disclosure of Interests: Charis Meng: None declared, Marie-France Valois: None declared, Julia Caci: None declared, Yvonne Lee medical writer on author's behalf for Sanofi Genzyme and Eli Lilly, Pfizer Aspire Grant, Hugues Allard-Chamard AstraZeneca, Abbvie, Amgen, Astrazeneca, BMS, Celltrion, Eli Lilly, GSK, Hoffmann-La Roche, Janssen, Novartis, Otsuka, Sandoz, Pfizer, Sobi, AstraZeneca, Eli Lilly, Fresenius Kabi, Pfizer, Bindee Kuriya Abbvie, UCB, Pfizer, Louis Bessette Amgen, BMS, Janssen, UCB, Abbvie, Pfizer, Celgene, Lilly, Novartis, Sanofi, TEVA, Fresenius Kabi, Sandoz, Organon, Sobi, BMS, Biocon, Pfizer, Glen Hazlewood: None declared, Carol A Hitchon Pfizer, Astra Zeneca, Carter Thorne cartho@rogers.com AbbVie, Acccord, BIOGEN, Pfizer, Roche, Medexus, Nordic, Organon, JAMP, Pfizer (Ph4 - observational), no clinical trials in 10 years, Janet Pope AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Certa, Eli Lilly, Frensenius Kabi, Janssen, Nordic Pharma, Novartis, Organon, Otsuka, Palleon, Pfizer, Sandoz, Sanofi, UCB, Zura DSMB: Astra Zeneca, Horizon, Novartis, AbbVie, Amgen, Astra Zeneca, Boehringer Ingelheim, Boxer Capital, Bristol Myers Squibb, Celltrion Healthcare, Eli Lilly, Frensenius Kabi, GSK, Janssen, Merck, Novartis, Pfizer, Sandoz, Sanofi, BMS, Janssen, Mallinckrodt, Pfizer (Seattle Genetics), Gilles Boire Abbvie, Janssen, Lilly, Mylan, Novartis, Pfizer, Sanofi, Teva, Viatris, BMS, Biocon, Pfizer, Susan J. Bartlett Sandoz, Nordic, Vivian Bykerk BMS, Pfizer, Abbvie, ER Squibb & Sons, BMS. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".