Tumor necrosis factor α antagonist use and cancer in patients with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: Concerns persist about a possible association between tumor necrosis factor alpha (TNFalpha) antagonist treatment and development of cancers in patients with rheumatoid arthritis (RA). This study was undertaken to estimate the association between treatment with biologic disease-modifying antirheumatic drugs (DMARDs) and development of cancer in patients with RA. METHODS: We conducted a cohort study pooling administrative databases from 2 US states and 1 Canadian province. A cohort of patients who had received a diagnosis of RA on > or =1 occasion and had been prescribed DMARDs was identified. We categorized patients with a prescription for a biologic DMARD as biologic DMARD users, and those with a prescription for methotrexate (MTX) but no biologic DMARD as MTX users. We used time-varying propensity scores to adjust for the large number of possible confounders and stratified proportional hazards regression to estimate the effects of biologic DMARDs on cancer. The primary end points were hematologic malignancies (lymphoma, multiple myeloma, and leukemia) and common solid tumors (colorectal, lung, stomach, breast, prostate, uterine, ovarian, urinary tract/bladder, and melanoma). RESULTS: The pooled cohort included 1,152 biologic DMARD users and 7,306 MTX users. We identified 11 hematologic malignancies and 46 solid tumors during 2,940 person-years of biologic DMARD use, and 88 hematologic malignancies and 558 solid tumors during 30,300 person-years of MTX use. Comparing biologic DMARD users with MTX users, the propensity score-adjusted pooled hazard ratio was 1.37 (95% confidence interval 0.71-2.65) for hematologic malignancies and 0.91 (95% confidence interval 0.65-1.26) for solid tumors. CONCLUSION: Our results indicate that users of biologic agents are unlikely to have a substantial increase in the risk of hematologic malignancies and solid tumors as compared with MTX users. Despite the use of large combined data sets, studying the effect of an infrequent exposure (biologic DMARDs) on rare diseases (hematologic malignancies) remains a challenge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".