Do Poor Prognostic Factors in Rheumatoid Arthritis Affect Treatment Choices and Outcomes? Analysis of a US Rheumatoid Arthritis Registry
Bibliographic record
Abstract
OBJECTIVE: To characterize patients with rheumatoid arthritis (RA) by number of poor prognostic factors (PPF: functional limitation, extraarticular disease, seropositivity, erosions) and evaluate treatment acceleration, clinical outcomes, and work status over 12 months by number of PPF. METHODS: Using the Corrona RA registry (January 2005-December 2015), biologic-naive patients with diagnosed RA having 12-month (± 3 mos) followup were identified and categorized by PPF (0-1, 2, ≥ 3). Changes in medication, Clinical Disease Activity Index (CDAI), and work status (baseline-12 mos) were evaluated using linear and logistic regression models. RESULTS: There were 3458 patients who met the selection criteria: 1489 (43.1%), 1214 (35.1%), and 755 (21.8%) had 0-1, 2, or ≥ 3 PPF, respectively. At baseline, patients with ≥ 3 PPF were older, and had longer RA duration and higher CDAI versus those with 0-1 PPF. In 0-1, 2, and ≥ 3 PPF groups, respectively, 20.9%, 23.2%, and 26.5% of patients received ≥ 1 biologic (p = 0.011). Biologic/targeted synthetic disease-modifying antirheumatic drug (tsDMARD) use was similar in patients with/without PPF (p = 0.57). After adjusting for baseline CDAI, mean (standard error) change in CDAI was -4.95 (0.24), -4.53 (0.27), and -2.52 (0.34) for 0-1, 2, and ≥ 3 PPF groups, respectively. More patients were working at baseline but not at 12-month followup in 2 (13.9%) and ≥ 3 (12.5%) versus 0-1 (7.3%) PPF group. CONCLUSION: Despite high disease activity and worse clinical outcomes, number of PPF did not significantly predict biologic/tsDMARD use. This may warrant reconsideration of the importance of PPF in treat-to-target approaches.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".