Effectiveness of Tocilizumab in Patients with Rheumatoid Arthritis Is Unaffected by Comorbidity Burden or Obesity: Data from a US Registry
Bibliographic record
Abstract
OBJECTIVE: Comorbidity burden and obesity may affect treatment response in patients with rheumatoid arthritis (RA). Few real-world studies have evaluated the effect of comorbidity burden or obesity on the effectiveness of tocilizumab (TCZ). This study evaluated TCZ effectiveness in treating RA patients with high versus low comorbidity burden and obesity versus nonobesity in US clinical practice. METHODS: Patients in the Corrona RA registry who initiated TCZ were stratified by low or high comorbidity burden using a modified Charlson Comorbidity Index (mCCI) and by obese or nonobese status using body mass index (BMI). Improvements in disease activity and functionality after TCZ initiation were compared for the above strata of patients at 6 and 12 months after adjusting for statistically significant differences in baseline characteristics. RESULTS: We identified patients with high (mCCI ≥ 2; n = 195) and low (mCCI < 2; n = 575) comorbidity burden and patients categorized as obese (BMI ≥ 30; n = 356) and nonobese (BMI < 30; n = 449) who were treated with TCZ. Most patients (> 95%) were biologic experienced and about one-third of patients received TCZ as monotherapy, with no significant differences between patients by comorbidity burden or obesity status. Improvement in disease activity and functionality at 6 and 12 months was similar between groups, regardless of comorbidity burden or obesity status. CONCLUSION: In this real-world analysis, TCZ was frequently used to treat patients with high comorbidity burden or obesity. Effectiveness of TCZ did not differ by comorbidity or obesity status.
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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 | 0.012 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".