The impact of different (rheumatoid) arthritis phenotypes on patients’ lives
Bibliographic record
Abstract
OBJECTIVES: To compare patient-reported outcome (PRO) domains between three arthritis phenotypes [undifferentiated arthritis (UA), autoantibody-negative RA (RA-) and autoantibody-positive RA (RA+)] at diagnosis, after 2 years and over time. METHODS: All UA (n = 130), RA- (n = 176) and RA+ (n = 331) patients from the tREACH trial, a stratified single-blinded trial with a treat-to-target approach, were used. PRO comparisons between phenotypes at baseline and after 2 years were performed with analysis of variance, while a linear mixed model compared them over time. Effect sizes were weighted against the minimal clinically important differences (MCIDs) for each PRO. RESULTS: RA- patients had a higher disease burden compared with RA+ and UA. At baseline and after 2 years, RA- patients had more functional impairment and a poorer Physical Component Summary (PCS) compared with the other phenotypes, while they only scored worse for general health and morning stiffness duration at baseline. The MCIDs were exceeded at baseline, except for functional ability between RA+ and UA, while after 2 years only the MCID of the PCS was exceeded by RA- compared with UA and RA. After 2 years the PROs of all phenotypes improved, but PROs measuring functioning were still worse compared with the general population, even when patients had low disease activity. CONCLUSION: RA- patients had the highest disease burden of all phenotypes. Although most patients have low disease activity after treatment, all clinical phenotypes still have a similar significant impact on patients' lives, which is mainly physical. Therefore it is important to assess and address PROs in daily practice because of persistent disease burden despite low disease activity. TRIAL REGISTRATION: ISRCTN26791028.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".