Photopatch Testing in Chinese Patients Over 10 Years
Bibliographic record
Abstract
BACKGROUND: Photoallergic contact dermatitis (PACD) is a hypersensitivity reaction that occurs when a previously photosensitized exogenous agent comes into contact with UV radiation. The best method for testing PACD is photopatch testing (PPT). OBJECTIVES: The primary objective of the study was to determine the frequency of PACD to 20 different photoallergens in common usage in China during a 10-year period. METHODS: All patients (n = 6153) who had PPTs done between 2005 and 2014 at the Department of Dermatology of Huashan Hospital, Fudan University, were included. RESULTS: A total of 3767 PACD reactions in 3668 subjects (59.61%) were recorded. Of these allergens, chlorpromazine (CPZ) (51.82%), para-aminobenzoic acid (11.94%), thimerosal (9.81%), potassium dichromate (6.37%), sulfanilamide (5.38%), and formaldehyde (4.7%) were the top 6 allergens that elicited PACD reactions. A comparison of PACD reactions between January 2005 to December 2009 and January 2010 to December 2014 revealed a statistically significant decrease in PACD reaction for chlorpromazine, potassium dichromate, p-aminobenzoic acid, and p-phenylenediamine. Formaldehyde showed a trend toward a statistically significant increase in sensitization over the 10-year period. CONCLUSIONS: In conclusion, positive PACD reactions were most frequent to chlorpromazine in our population. New allergens such as potassium dichromate and formaldehyde should be added to the test series.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".