Prolonged Remission of Marginal Zone Lymphoma in a Patient with Rheumatoid Arthritis Treated with Anti-tumor Necrosis Factor Agents
Bibliographic record
Abstract
To the Editor: Anti-tumor necrosis factor (TNF) agents are widely used in the treatment of rheumatoid arthritis (RA). Some randomized controlled trials and a metaanalysis have raised the possibility of an increased occurrence of cancer associated with anti-TNF therapy1. In the context of hematological malignancies, there are reports of myeloma, as well as monoclonal gammopathy of undetermined significance (MGUS), progressing while receiving anti-TNF treatment2. The British Society for Rheumatology states that there is no conclusive evidence for an increase in risk of solid tumors or lymphoproliferative disease with anti-TNF therapies above what would be expected for the rest of the RA population3. Further, several studies have reported anti-TNF working against solid cancers4,5. We report a case of marginal zone lymphoma (MZL) that appeared to respond to sequential anti-TNF therapy with infliximab (IFX) and etanercept (ETN) in a patient with RA. A 55-year-old woman was diagnosed with RA in 1991 and was noted to have a lymphocytosis (white cell count 20 × 109/l) when being considered for IFX therapy in September 2002. This was confirmed as an MZL following … Address correspondence to Dr. S.L. Donaldson, 25 Oakdale Glen, Harrogate, HG1 2JY, North Yorkshire, UK. E-mail: s.donaldson{at}doctors.org.uk
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.003 |
| 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".