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Oral lichen planus: a novel staging and algorithmic approach and all that is essential to know

2020· preprint· en· W3013208186 on OpenAlexaff
Eran Shavit, Klieb Hagen, Neil H. Shear

Bibliographic record

VenueF1000Research · 2020
Typepreprint
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsOpen peer reviewPlant biologyLichenPhysiologyMedicineBiologyDermatologyBotany

Abstract

fetched live from OpenAlex

Lichen planus (LP) is a chronic autoimmune disease. Oral lesions may occur in isolation or in combination with other affected muco-cutaneous sites. Oral LP (OLP) may present as one of the various manifestations of LP or may present as a disease sui generis with a broad range of severity. Despite this disease targeting the oral mucosa, its great impact on quality of life is underrated. In this article, we provide an updated review of the pathophysiology and epidemiology of OLP and offer guidance for its diagnosis and management. We also propose an algorithmic approach to the clinical forms of OLP and a novel staging system to facilitate management decisions.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.002
Science and technology studies0.0020.003
Scholarly communication0.0050.008
Open science0.0020.004
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0040.003

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 head0.157
GPT teacher head0.426
Teacher spread0.269 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations38
Published2020
Admission routes1
Has abstractyes

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