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Record W2427957355 · doi:10.1097/der.0000000000000204

Amalgam Contact Allergy in Oral Lichenoid Lesions

2016· article· en· W2427957355 on OpenAlexvenueno aff
Apichaya Thanyavuthi, Waranya Boonchai, Pranee Kasemsarn

Bibliographic record

VenueDermatitis · 2016
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePatch testAmalgam (chemistry)DentistryContact allergyAllergyDermatologyOral lichen planusRetrospective cohort studyAllergenContact dermatitisOral mucosaInternal medicinePathologyImmunology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.018
GPT teacher head0.260
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2016
Admission routes1
Has abstractyes

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