069. Prognostic Factors for Intravenous Abatacept Retention in Patients Who have Received at Least One Prior Biologic Agent: 2-Year Results from a Prospective, International, Real-World Study
Bibliographic record
Abstract
Background: To identify prognostic factors of retention for abatacept (ABA) treatment in patients with moderate-to-severe RA, using final results from the real-world ACTION study. Methods: ACTION was a 2-year follow up, non-interventional, international, multicentre, cohort study that evaluated retention and effectiveness of i.v. ABA in adults with moderate-to-severe RA in Europe and Canada (May 2008 to January 2011). Socio-demographics, disease characteristics, previous/concomitant therapies and comorbidities at ABA initiation were considered potential prognostic variables of retention. Patients who had received ≥1 prior biologic agent in countries with sufficient patient numbers to explore between-country effects were included. Clinically relevant variables, known risk factors and prognostic factors with a p ≤ 0.10 (univariate analysis) were entered into a multivariate Cox proportional hazards regression model, with clustered data adjusted for one investigator. Factors with p ≤ 0.10 after backward selection were retained in the final model. Co-linearity and interactions were assessed. Additional analysis to account for missing data in covariates was performed using multiple imputation by chained equations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".