Physician- and Patient-reported Effectiveness Are Similar for Tofacitinib and TNFi in Rheumatoid Arthritis: Data From a Rheumatoid Arthritis Registry
Bibliographic record
Abstract
OBJECTIVE: Tofacitinib (TOF) is an oral, small-molecule drug used for rheumatoid arthritis (RA) treatment and is one of several alternative treatments to tumor necrosis factor inhibitors (TNFi). We evaluated physician- and patient-reported effectiveness of TNFi compared to TOF, using real-world data from the Ontario Best Practices Research Initiative (OBRI). METHODS: Patients enrolled in the OBRI initiating TOF or TNFi between 2014 and 2019 were included. Patients were required to have physician- and patient-reported effectiveness outcome data, including Clinical Disease Activity Index (CDAI) and RA Disease Activity Index (RADAI), available at treatment initiation and 6 (± 2) months later. To deal with confounding by indication, we estimated propensity scores (PS) for covariates. RESULTS: Four hundred nineteen patients were included. Of those, 226 initiated a TNFi and 193 TOF, and had a mean (SD) disease duration of 8.0 (8.7) and 12.6 (9.6) years, respectively. In addition, the TNFi group was less likely to have prior biologic use (21.7%) compared to the TOF group (67.9%). The proportion of patients in CDAI low disease activity (LDA)/remission (REM) at 6 months was 36.7% and 33.2% in the TNFi and TOF groups, respectively. The generalized linear mixed models adjusting for PS quantile showed that there was no significant difference in CDAI LDA/REM (odds ratio [OR] 0.85, 95% CI 0.51-1.43) and RADAI coefficient (OR 0.48, 95% CI -0.18 to 1.14) between the 2 groups (ref: TOF). CONCLUSION: In patients with RA, physician- and patient-reported effectiveness are similar in the TNFi and TOF groups 6 months after treatment.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".