Methotrexate and Patch Testing: A Retrospective Review
Bibliographic record
Abstract
BACKGROUND: Patch testing while taking systemic immunosuppressants is sometimes unavoidable. Methotrexate (MTX) is the immunosuppressant currently considered least likely to negatively impact patch testing. OBJECTIVE: The aim of the study was to characterize a cohort of patients patch tested while taking MTX. METHODS: This is a retrospective review of patients patch tested at the University of North Carolina Dermatology in Chapel Hill, North Carolina, from 2010 to 2019, comparing patch test results of patients taking MTX with those of a control group. RESULTS: An overall 52.8% positivity rate (356/674) was observed. Sixty two of 674 patients were patch tested while taking MTX, with a 46.8% positivity rate (29/62) compared with 53.4% (327/612) in the control group. The control group experienced 975 reactions, including 637 1+ reactions, 291 2+ reactions, and 47 3+ reactions. The MTX group experienced 50 reactions, including thirty-two 1+ reactions, fourteen 2+ reactions, and four 3+ reactions. The difference between the distributions was not statistically significant. Mean weekly MTX dose was 15.6 mg, whereas mean total accumulated dose was 251.6 mg. There was no statistically significant difference between weekly dose and total accumulated dose in patients with positive or negative results. CONCLUSIONS: In our cohort, MTX had no discernible effect on patch test results, supporting use during patch testing with minimal false-negative risk.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".