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Record W2325865192 · doi:10.2310/7750.2008.08017

Results of Patch Testing in Patients Diagnosed with Oral Lichen Planus

2009· article· en· W2325865192 on OpenAlexaff
Mark Lomaga, Shely Polak, Miriam Grushka, Scott R. Walsh

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2009
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsWilliam Osler Health SystemYork UniversityUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicinePatch testOral lichen planusDermatologyContact allergyPatch testingAllergyContact dermatitisAllergic contact dermatitisAllergenImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Oral lichenoid lesions (OLLs) resemble oral lichen planus (OLP) but develop secondary to various underlying causes. The role of contact allergy in precipitating and/or perpetuating OLL is well documented but remains controversial. OBJECTIVE: To help elucidate the association of contact allergy and OLL, we reviewed patch-test readings in patients diagnosed with OLP-like lesions. METHODS: We retrospectively reviewed patients diagnosed with OLP-like lesions who had patch tests performed between January 1, 2006, and December 31, 2007. RESULTS: Patch tests were performed on 24 patients with a histopathologic and/or clinical diagnosis of OLP. Of these, 16 (67%) had positive patch-test readings. At least eight (50%) of these patients had clinically relevant reactions. Ten of the 16 patients (63%) had reactions to metals. In most of these patients, troublesome areas tended to localize adjacent to metallic dental restorations. Of the nine patients (56%) who had reactions to fragrances, flavorings, gallates, and/or diallyl disulfide, the majority improved after avoiding these allergens. CONCLUSION: Our findings support the notion that contact allergy may underlie the pathogenesis of OLL and that allergen avoidance may result in amelioration of disease.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.040
GPT teacher head0.301
Teacher spread0.260 · 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.

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

Citations19
Published2009
Admission routes1
Has abstractyes

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