The impact of seropositivity on the effectiveness of biologic anti-rheumatic agents: results from a collaboration of 16 registries
Bibliographic record
Abstract
OBJECTIVES: RF and ACPA are used as diagnostic tools and their presence has been associated with clinical response to some biologic DMARDs (bDMARDs) in RA. This study compared the impact of seropositivity on drug discontinuation and effectiveness of bDMARDs in patients with RA, using head-to-head comparisons in a real-world setting. METHODS: We conducted a pooled analysis of 16 observational RA registries. Inclusion criteria were a diagnosis of RA, initiation of treatment with rituximab (RTX), abatacept (ABA), tocilizumab (TCZ) or TNF inhibitors (TNFis) and available information on RF and/or ACPA status. Drug discontinuation was analysed using Cox regression, including drug, seropositivity, their interaction, adjusting for concomitant and past treatments and patient and disease characteristics and accounting for country and calendar year of bDMARD initiation. Effectiveness was analysed using the Clinical Disease Activity Index evolution over time. RESULTS: Among the 27 583 eligible patients, the association of seropositivity with drug discontinuation differed across bDMARDs (P for interaction <0.001). The adjusted hazard ratios for seropositive compared with seronegative patients were 1.01 (95% CI 0.95, 1.07) for TNFis, 0.89 (0.78, 1.02)] for TCZ, 0.80 (0.72, 0.88) for ABA and 0.70 (0.59, 0.84) for RTX. Adjusted differences in remission and low disease activity rates between seropositive and seronegative patients followed the same pattern, with no difference in TNFis, a small difference in TCZ, a larger difference in ABA and the largest difference in RTX (Lundex remission difference +5.9%, low disease activity difference +11.6%). CONCLUSION: Seropositivity was associated with increased effectiveness of non-TNFi bDMARDs, especially RTX and ABA, but not TNFis.
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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 | 0.061 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".