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Record W3108329783 · doi:10.1097/der.0000000000000703

Can We Separate Oral Lichen Planus from Allergic Contact Dermatitis and Should We Patch Test? A Systematic Review of Chronic Oral Lichenoid Lesions

2020· review· en· W3108329783 on OpenAlexaffvenue
Shahmina Rahat, Nadia Kashetsky, Ahmed Bagit, Muskaan Sachdeva, Yuliya Lytvyn, Asfandyar Mufti, Howard I. Maibach, Jensen Yeung

Bibliographic record

VenueDermatitis · 2020
Typereview
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsBrock UniversityMemorial University of NewfoundlandWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineOral lichen planusAmalgam (chemistry)Patch testDermatologyDentistryPatch testingContact dermatitisAllergy

Abstract

fetched live from OpenAlex

This systematic review summarizes characteristics and treatment outcomes of dental amalgam-associated oral lichenoid lesions (OLLs) and oral lichen planus (OLP). Embase and MEDLINE were searched for original studies on OLLs or OLP associated with dental amalgam. Data extraction was completed from 44 studies representing 1855 patients. Removal of amalgam restorations led to complete resolution in 54.2% (n = 423/781), partial resolution in 34.8% (n = 272/781), and no resolution in 11.0% (n = 86/781) of the patients with OLLs, whereas complete resolution occurred in 37.1% (n = 72/194), partial resolution in 26.3% (n = 51/194), and no resolution in 36.6% (n = 71/194) of the patients with OLP. For patients with OLLs, 91.6% of the patients with positive patch tests and 82.9% with negative patch tests had improvement with removal of amalgam, whereas for patients with OLP, 89.2% of the patients with positive patch tests and 78.9% with negative patch tests had improvement with removal of amalgam. Our results suggest improvement occurs, regardless of patch testing status.

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)0.006
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0100.011
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.318
Teacher spread0.267 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2020
Admission routes2
Has abstractyes

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