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Record W1965511599 · doi:10.2310/6620.2004.03021

Recovery from Mercury-Induced Burning Mouth Syndrome Due to Mercury Allergy

2004· article· en· W1965511599 on OpenAlexvenueno aff
Paolo D. Pigatto, Gianpaolo Guzzi, Paola Persichini, Sherry Barbadillo

Bibliographic record

VenueDermatitis · 2004
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMercury (programming language)DermatologyAllergyBurning mouth syndromeAllergic contact dermatitisPatch testingContact dermatitisNickel allergyPatch testContact allergyAllergenOral mucosaAtopic dermatitisDentistryImmunologyPathology

Abstract

fetched live from OpenAlex

We report a case in which burning mouth syndrome (BMS) was associated with a strong allergy to mercury. The aim of this case history is to strengthen knowledge of the relationship among allergy to mercury, systemic allergic contact dermatitis, and hypersensitivity of the oral mucosa. We performed series of standard and dental patch tests for screening for contact allergy to dental materials, in accordance with International Contact Dermatitis Research Group guidelines. Positive extreme allergic reactions to mercury (+++) and amalgam (++) were seen at the patch site and caused a flare-up of the systemic erythematous reaction. Full recovery from BMS and complete remission of systemic dermatitis were achieved after the mercury tooth filling was removed. Mercury is thought to be an allergen implicated in BMS as well as in the systemic reactivation of allergic contact dermatitis. Patch testing with dental series seems to have greater sensitivity and relevance in BMS patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.015
GPT teacher head0.239
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations33
Published2004
Admission routes1
Has abstractyes

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