Photopatch testing: Clinical characteristics, test results, and final diagnoses from the North American Contact Dermatitis Group, 2009–2020
Bibliographic record
Abstract
BACKGROUND: Photoallergic contact dermatitis (PACD) is a delayed hypersensitivity reaction to allergens only in the presence of ultraviolet radiation in sunlight. Photopatch testing (PhotoPT) is necessary to confirm the diagnosis of PACD. There are few published studies of PhotoPT in North America. OBJECTIVE: To summarise the results of patients photopatch tested by members of the North American Contact Dermatitis Group (NACDG), 2009-2020. METHODS: Retrospective analysis of patient characteristics and PhotoPT results to 32 allergens on the NACDG Photopatch Test Series. RESULTS: Most of the 454 tested patients were female (70.3%), 21-60 years old (66.7%) and White (66.7%). There were a total of 119 positive photopatch tests. Sunscreen agents comprised 88.2% of those, with benzophenones responsible for over half of them. Final diagnoses included PACD in 17.2%, allergic contact dermatitis (ACD) in 44.5%, polymorphous light eruption (PMLE) in 18.9% and chronic actinic dermatitis (CAD) in 9.0% of patients. CONCLUSIONS: In 454 patients with suspected photosensitivity referred for photopatch testing in North America, approximately one-fifth had PACD. Sunscreen agents, especially benzophenones, were the most common photoallergens. Other common diagnoses included ACD, PMLE and CAD. Photopatch testing is an important tool for differentiating these conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".