P120 Characteristics of difficult-to-treat rheumatoid arthritis: results from a systematic literature review
Bibliographic record
Abstract
Abstract Background/Aims Estimates predict between 10-20% of RA patients meet difficult-to-treat Rheumatoid Arthritis (D2T-RA) classification, representing a clinically distinct cohort. There is a significant unmet need in the identification and management of these patients. Discerning significant characteristics across cohorts may identify predictors for D2T-RA, which may prompt earlier clinical recognition and patient-tailored, holistic management to improve clinical outcomes. This review aims to describe the clinical characteristics of patients with EULAR-defined D2T-RA to help identify and manage predictors, contributory factors, or consequences of difficult-to-treat disease. Methods A systematic literature search of PubMed, Embase and Web of Science databases for studies pertaining to characteristics of D2T-RA was undertaken from database inception to May 2023. Results were screened against strict inclusion/exclusion criteria. Risk-of-bias (RoB) assessments were performed using the Newcastle-Ottawa Scale (NOS) and a modified version for cross-sectional studies (mNOS). Significant (p < 0.05) baseline characteristics between D2T-RA and non-D2T patients were extracted for comparison following thematic analysis. Where there was conflict in the direction of the extracted characteristics; the number and quality of studies were used to resolve evidential disagreements. Results 1687 studies were identified, of which seven fulfilled the inclusion criteria. Included studies were observational, published between 2021 and 2023, with mean D2T and non-D2T sample sizes of 83 and 417 patients, respectively. Five of the seven cohorts were European, with two from Japan. Following RoB and dispute resolution, all seven identified studies were included for analysis. Key themes elucidated are shown in table 1. Of note, significant characteristics included female sex, autoantibody positivity, greater annual bone erosion progression, interstitial lung disease, mental health conditions, and non-adherence. Conclusion Greater awareness of clinical characteristics of D2T-RA patients will assist clinicians by promoting the assessment of traits associated with poor treatment response. This is the first review to systematically describe characteristics of D2T-RA patients across multiple cohorts. Several commonly recorded, modifiable characteristics were associated with D2T-RA. Evidential disagreements and heterogeneous study designs were analytical barriers, and clinical intervention studies are needed to determine if addressing these factors in the routine clinical setting improves clinical outcomes and prevention in D2T-RA. Disclosure S. Chambers: None. A.P. Croft: None. R. Singh: None.
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 | 0.017 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".