Patch-Testing While on Systemic Immunosuppressants
Bibliographic record
Abstract
BACKGROUND: Occasionally, the need arises to patch-test patients while they are on immunomodulators. Little is known about how these systemic agents affect the results of patch testing. OBJECTIVE: To present data on 11 patients who underwent patch testing while under the effects of immunosuppressants. METHODS: Retrospective chart reviews were performed on 11 patients who underwent patch testing while they were taking various systemic immunosuppressants or within 48 hours of cessation of various systemic immunosuppressants. RESULTS: Patients had been taking prednisone (n = 6), cyclosporine (n = 2), combination cyclosporine and prednisone (n = 1), mycophenolate mofetil (n = 1), and infliximab (n = 1) up to 48 hours prior to and/or during patch testing. Seven patients showed at least one strong (++) or extreme (+++) patch-test reaction. Three patients had at least one weak (+) reaction. One patient showed only questionable reactions. The patient on mycophenolate was eventually retested while off immunosuppressants and showed strong clinically relevant patch-test reactions. Overall, 8 of the 11 patients reported some improvement in their dermatitis, including all the patients with strong or extreme reactions. CONCLUSION: While it is optimal for patch testing to be performed when patients are off immunosuppressants, immunosuppressive therapies should not be an absolute contraindication to patch testing.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".