Development of neoplasms in pediatric patients with rheumatic disease exposed to anti-tumor necrosis factor therapies: a single Centre retrospective study
Bibliographic record
Abstract
BACKGROUND: Anti-TNF (Tumor necrosis factor) therapy is effective in treating pediatric patients with refractory rheumatic disease. There is however a concern that anti-TNF usage may increase the risk of malignancy. Reports on specific types of malignancy in this patient population have been emerging over the past decade, but there is a need for additional malignancy reports, as these events are rare. Therefore, a retrospective chart review was performed on the biologic database of pediatric rheumatology patients at The Hospital for Sick Children (SickKids) from 1997 to 2013 for neoplasms, patient demographic information and rheumatologic treatment course. FINDINGS: 6/357 (1.68%) rheumatology patients treated with anti-TNF therapy between 1997 and 2013 developed neoplasms. One patient had two malignancies. One patient had a benign neoplasm. Cases were exposed to etanercept, infliximab or both. Neoplasms developed late after anti-TNF exposure (median 5.0 years) and infliximab treatment was associated with a shorter time to malignancy. The neoplasms identified were as follows: 2 renal clear cell carcinoma, 1 pilomatricoma, 1 nasopharyngeal carcinoma, 1 Ewing's sarcoma, 1 hepatic T-cell lymphoma, 1 lymphoproliferative disease. CONCLUSIONS: The malignancy rate at our centre is low, however more than half of the neoplasms identified were rare and unusual in the pediatric population. The 5-year malignancy-free probability for patients with juvenile idiopathic arthritis (JIA) treated with biologic therapy was 97% from our database. Long-term screening for rare neoplasms is important as part of the safety monitoring for any pediatric rheumatology patient receiving anti-TNF therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".