Patch Testing with a Large Series of Metal Allergens: Findings from More Than 1,000 Patients in One Decade at Mayo Clinic
Bibliographic record
Abstract
BACKGROUND: The standard allergen series used in patch testing contains metals that most commonly cause allergic contact dermatitis, but testing with additional metal allergens is warranted for select patients. OBJECTIVE: To report our experience with patch testing of metals. METHODS: We retrospectively analyzed outcomes of 1,112 patients suspected of having metal allergies. Patients were seen from January 1, 2000, through December 31, 2009. Patch testing was performed with 42 metal preparations (6 in the standard series, 36 in the metal series). RESULTS: Patch testing most commonly was performed for patients with oral disease (almost half the patients), hand dermatitis, generalized dermatitis, and dermatitis affecting the lips, legs, arms, trunk, or face. At least one positive reaction was reported for 633 patients (57%). Metals with the highest allergic patch-test reaction rates were nickel, gold, manganese, palladium, cobalt, Ticonium, mercury, beryllium, chromium, and silver. Metals causing no allergic patch-test reactions were titanium, Vitallium, and aluminum powder. Metals with extremely low rates of allergic patch-test reactions included zinc, ferric chloride, and tin. Reaction rates varied depending on metal salt, concentration, and timing of readings. CONCLUSION: Many metals not in the standard series were associated with allergic patch-test reactions. The many questions raised by these findings, concerning patch testing with individual metals, will be the subject of future studies.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".