Photopatch Testing of 182 Patients: A 6-Year Experience at the Mayo Clinic
Bibliographic record
Abstract
BACKGROUND: Photopatch testing is important for diagnosing photoallergic contact dermatitis. Although results of photopatch testing have been presented from many European centers, there have been few reports of the results of photopatch testing in the United States. OBJECTIVE: To review the Mayo Clinic's recent experience with photopatch testing, identify common photoallergens, and compare our current and previous findings. METHODS: We retrospectively reviewed records of patients who underwent photopatch testing at the Mayo Clinic between January 1, 2000, and December 31, 2005 (N = 182). RESULTS: Fifty-four patients (29.7%) had photoallergic contact reactions, and 29 (15.9%) had allergic contact reactions. The most common photoallergens were medications, sunscreen agents, fragrances, and antiseptics. CONCLUSION: Photopatch testing is the technique useful in identifying photoallergens. The series of allergens used must be constantly updated to reflect newly identified and outdated photoallergens. We present a 6-year experience with photopatch testing. Medications, sunscreen agents, fragrances, and antiseptics were the most frequently identified photoallergens.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".