Effect of the Application of Trial Inclusion Criteria on the Efficacy of Adalimumab Therapy in a Rheumatoid Arthritis Cohort
Bibliographic record
Abstract
OBJECTIVE: To evaluate the influence of inclusion criteria used in rheumatoid arthritis (RA) trials with adalimumab on clinical outcome and response. METHODS: The different inclusion criteria of published trials of adalimumab in RA were separately applied to a large prospective cohort of patients with RA treated with adalimumab (AdRA cohort), thereby mimicking patient selection for a clinical trial. Clinical response and outcome in the resulting 11 projection groups were compared using the 28-joint Disease Activity Score (DAS28) and time-averaged DAS28 as outcome measures of efficacy. RESULTS: Thirteen trials (n = 54-799) with 11 different sets of entry criteria were identified, resulting in 11 projection groups (n = 22-168). The DAS28 at baseline was similar in the original trial and each projection group based on this trial (5.1-6.4, total AdRA cohort 5.1). After 28 weeks, the efficacy varied substantially among the 11 projected groups (change from baseline DAS28: -1.65 to -2.65, time-averaged DAS28 3.67-4.53). Expressed as outcome (DAS28 at 28 weeks), the efficacy was much more similar for almost all projection groups (3.5-4.0) and thus appeared to be mostly independent of disease activity at baseline. CONCLUSION: We observed that different inclusion criteria for clinical trials can have a marked effect on the expected response, i.e., improvement from baseline. A novel finding is that final disease activity appeared much less dependent on initial disease activity. Our study suggests that for daily practice, one can assume that adalimumab treatment will on average result in a DAS28 between 3.5 and 4.0 after 28 weeks of treatment, regardless of baseline disease activity.
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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 | 0.575 | 0.694 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".