Patch Testing Results from the Massachusetts General Hospital Occupational and Contact Dermatitis Clinic, 2017–2022
Bibliographic record
Abstract
Abstract: Background: Patch testing is gold standard for identifying the source of allergic contact dermatitis (ACD). Objective: To report patch testing results from the Massachusetts General Hospital (MGH) Occupational and Contact Dermatitis Clinic from 2017 to 2022. Methods: Retrospective analysis of patients referred to MGH for patch testing, 2017–2022. Results: In total, 1438 patients were included. At least 1 positive patch test (PPT) reaction was observed in 1168 (81.2%) patients and at least 1 relevant PPT reaction was observed in 1087 (75.6%) patients. The most common allergen with a PPT was nickel (21.5%), followed by hydroperoxides of linalool (20.4%) and balsam of Peru (11.5%). Sensitization rates statistically increased over time for propylene glycol and decreased for 12 other allergens (all P values <0.0004). Limitations: Retrospective design, single institution tertiary referral population, and variations in allergens and suppliers across the study period. Conclusion: The field of ACD is constantly evolving. Regular analysis of patch test data is crucial to identify emerging and diminishing contact allergen trends.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".