Incidence of Malignancies in a Cohort of Psoriatic Arthritis Patients Taking Traditional Disease Modifying Antirheumatic Drug and Tumor Necrosis Factor Inhibitor Therapy: An Observational Study
Bibliographic record
Abstract
OBJECTIVE: Psoriatic arthritis (PsA) is an inflammatory arthropathy, associated with skin and/or nail psoriasis. As suggested in 2012 by the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA), studies devoted to assess cancer in the PsA population are still limited and need to be increased. Therefore, the aim of this study was to determine the incidence of malignancies in patients with PsA who are taking conventional and biologic therapies. METHODS: A cohort of patients with PsA was followed prospectively. At first visit, as well as at each 3-4 month followup visit, according to standardized clinical practice, medical history, and physical and laboratory findings were recorded. Information on the presence of comorbidities, as well as malignancies, was collected. At each visit, data were recorded on radiography and pathology, confirming malignancy diagnosis, when present. RESULTS: A total of 618 patients with PsA were included in the study. In particular, 296 were taking anti-tumor necrosis factor-α (anti-TNF) agents and 322 were taking disease-modifying antirheumatic drugs (DMARD). During the observation period, in the total group, 44 patients (7.1%) had a diagnosis of malignancy. Of them, 14 (4.7%; 95% CI 2.8-7.8; 0.52/100 patient-yrs) received anti-TNF therapy and 30 (9.3%; 95% CI 6.6-13.0; 1.03/100 patient-yrs) received traditional DMARD (p = 0.019). However, after adjusting for major demographic and clinical characteristics, the difference between the 2 treatments was no longer significant (p = 0.480), and the only predictor of malignancy occurrence was age (HR 1.04, 95% CI 1.009-1.073, p = 0.012). CONCLUSION: Data from this study confirm that biological therapies do not lead to any increased risk for cancer development, when adequately administered and with proper followup.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".