Efficacy of biologic agents in improving the Health Assessment Questionnaire (HAQ) score in established and early rheumatoid arthritis: a meta-analysis with indirect comparisons.
Bibliographic record
Abstract
OBJECTIVES: The Health Assessment Questionnaire (HAQ) is a validated physical function measure. It is predictive for disability and mortality. The objective of this study was to determine the comparative efficacy of biologic agents in improving HAQ in patients with established RA who failed DMARDs or anti- TNF agents and in early RA (ERA). METHODS: We performed random effects meta-analyses of published randomised, placebo-controlled trials. Outcome was the mean difference in change in HAQ for biologic agents compared to controls (ΔHAQB-ΔHAQC). Indirect comparisons of the different biologic drugs were conducted using the Q-test based on analysis of variance. Meta-regression was performed using the method of moments. RESULTS: Twenty-eight trials were included: 19 with DMARD-failures; 4 with anti-TNF-failures and 5 ERA. The following biologics were represented: abatacept, adalimumab, certolizumab, etanercept, golimumab, infliximab, rituximab and tocilizumab. Efficacy of biologics at reducing HAQ was significantly different based on prior treatment (p=0.001). In RA patients with DMARD failures, ΔHAQB-ΔHAQC was -0.22; 95%CI: -0.24, -0.20 (I2=55%). Infliximab, abatacept and tocilizumab had lower ΔHAQB-ΔHAQC compared to other biologics (p<0.02). In anti-TNF-failures, ΔHAQB-ΔHAQC was -0.36; 95%CI: -0.42, -0.30 (I2=0%). In ERA, methotrexate-naïve trials, ΔHAQB-ΔHAQC was -0.19; 95% CI: -0.26, -0.13 (I2=0%). There were no significant differences in the efficacy of different biologics for anti-TNF failures and ERA. CONCLUSIONS: Biologic agents were efficacious at lowering HAQ in RA. Differences between agents in RA with DMARD failures were less than the minimally clinically important difference for HAQ; therefore, the clinical significance of these differences is unclear.
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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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.069 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".