Amalgam Contact Allergy in Oral Lichenoid Lesions
Bibliographic record
Abstract
BACKGROUND: Oral lichenoid lesions (OLLs) cannot be distinguished from oral lichen planus (OLP) except that causative factors can be identified. Amalgam is one of the causative allergens, and replacement may lead to resolution. OBJECTIVES: The aim of this study was to determine the prevalence, prognosis, and aggravating factors of amalgam contact allergy in patients with OLLs. METHODS: A clinical retrospective and prospective cohort study was carried out at the Dermatology Department, Siriraj Hospital, Mahidol University. In cases with patch test positive for an amalgam component, patients were suggested to replace their amalgam restorations. RESULTS: Of 53 patients with OLLs, 39 (73.6%) had positive patch test results, and 31 (58.5%) reacted to at least one amalgam component. The most common causative allergen was mercury (35.8%). Lesions on bilateral buccal mucosa and gingiva tended to have negative patch test results (P < 0.05). Spicy food was the main aggravating factor. Amalgam replacements were performed in 10 patients. Clinical improvement was observed in all cases with complete healing in 3 cases. CONCLUSIONS: The prevalence of amalgam contact allergy in patients with OLLs was 58.5%. Mercury was the most common allergen, followed by copper sulfate. An association between clinical, topographic relation, and positive patch test results would be a useful predictor for favorable outcome after amalgam removal.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".