Therapy with Biologic Agents After Diagnosis of Solid Malignancies: Results from the Corrona Registry
Bibliographic record
Abstract
OBJECTIVE: Guidelines suggest that rheumatoid arthritis (RA) patients with previously treated solid malignancy may be treated as patients without such history. The recommendation is based on limited evidence, and rheumatologists and patients are frequently hesitant to start or continue biologic therapy after a cancer diagnosis. The objective of this study was to describe biologic use in real-world patients with RA following a malignancy diagnosis. METHODS: RA patients enrolled in the Corrona registry and diagnosed with solid malignancy with at least 1 followup visit within 12 months after diagnosis were included in this analysis. The proportion of patients continuing or initiating biological/targeted synthetic disease-modifying antirheumatic drug (bDMARD/tsDMARD) after diagnosis was estimated. Median time to initiation of bDMARD/tsDMARD after diagnosis was calculated using the Kaplan-Meier method and the proportion initiating biologic treatment in 6-month time intervals was estimated using the life-table method. RESULTS: There were 880 patients who met inclusion criteria with 2585 person-years total followup time postdiagnosis. Of those, 367 (41.7%) were treated with bDMARD/tsDMARD within 12 months preceding malignancy, of whom 270 (30.7%) were taking such agents at first postdiagnosis visit. Forty-four (5%) switched biologic agents within 36 months and an additional 90 patients (10.2%) started a biologic. The majority of bDMARD/tsDMARD initiations during followup was a tumor necrosis factor inhibitor (TNFi; 53.5%). CONCLUSION: In real-world practice, nearly one-third of RA patients with a cancer diagnosis were treated with systemic therapy in the immediate visit after malignancy diagnosis and a considerable percentage of malignancy survivors initiated biologic therapy within 3 years. The majority of bDMARD/tsDMARD initiations post-malignancy diagnosis was a TNFi.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".