Patch-Testing with the Standard Series at the Massachusetts General Hospital, 1998 to 2006
Bibliographic record
Abstract
BACKGROUND: The diagnostic tool to detect allergic contact sensitization is patch testing. OBJECTIVE: Results of patch testing performed from January 1998 to December 2006 at the Massachusetts General Hospital (MGH) are analyzed and compared to our 1990-1997 data as well as to data from North American and European contact dermatitis societies. METHODS: Data were collected from retrospective chart reviews and analyzed, focusing on the Hermal standard tray. RESULTS: The most common sensitizers were fragrance mix (18.3%) and nickel (16.7%). Significant increases over time were seen for balsam of Peru (p < .0005; CI, 1.34-2.76) and wool alcohols (p = .002; CI, 1.38-4.38) while gender-related statistical predominance was seen for nickel in females (p < .0005; CI, 2.92-8.20) and for epoxy resin in males (p < .0005; CI, 0.14-0.58). Our findings are similar to those of the North American and European contact dermatitis societies. The retrospective study sample was drawn from a selected group of referred patients that may not be representative of the general population. Analysis of data focused on the Hermal standard tray and might not reflect trends resulting from additional allergens in supplemental trays. CONCLUSION: Sensitization rates and the most important allergens at MGH have been stable over the past 17 years.
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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 | 0.001 | 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) | 0.004 | 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".