Study design for a comprehensive assessment of biologic safety using multiple healthcare data systems
Bibliographic record
Abstract
BACKGROUND: Although biologic treatments have excellent efficacy for many autoimmune diseases, safety concerns persist. Understanding the absolute and comparative risks of adverse events in patient and disease subpopulations is critical for optimal prescribing of biologics. PURPOSE: The Safety Assessment of Biologic Therapy collaborative was federally funded to provide robust estimates of rates and relative risks of adverse events among biologics users using data from national Medicaid and Medicare plus Medicaid dual-eligible programs, Tennessee Medicaid, Kaiser Permanente, and state pharmaceutical assistance programs supplementing New Jersey and Pennsylvania Medicare programs. This report describes the organizational structure of the collaborative and the study population and methods. METHODS: This retrospective cohort study (1998-2007) examined risks of seven classes of adverse events in relation to biologic treatments prescribed for seven autoimmune diseases. Propensity scores were used to control for confounding and enabled pooling of individual-level data across data systems while concealing personal health information. Cox proportional hazard modeling was used to analyze study hypotheses. RESULTS: The cohort was composed of 159,000 subjects with rheumatic diseases, 33,000 with psoriasis, and 46,000 with inflammatory bowel disease. This report summarizes demographic characteristics and drug exposures. Separate reports will provide outcome definitions and estimated hazard ratios for adverse events. CONCLUSION: This comprehensive research will improve understanding of the safety of these treatments. The methods described may be useful to others planning similar evaluations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".