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Record W2330820803 · doi:10.2310/7750.2008.07096

Are All Seborrheic Keratoses Benign? Review of the Typical Lesion and Its Variants

2008· review· en· W2330820803 on OpenAlexaff
Kristin Noiles, Ronald Vender

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and rare skin diseases.
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineDermatologyLesionKeratosisSeborrheic keratosisPremalignant lesionActinic keratosesPathologyCancerBasal cellInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Seborrheic keratosis (SK) is one of the more common benign epidermal neoplasms seen in adult and middle-aged patients. OBJECTIVE: As little is written in the literature about the variants of SK, this article aims to categorize and discuss the different subtypes and their important associations. METHODS: An in-depth literature search using OVID Medline and PubMed was conducted to classify the various subtypes of SK. Clinical variants were photographed and used to help document the subtypes. The pathology is described for each. RESULTS: Six subtypes of SK were identified: dermatosis papulosa nigra, stucco keratosis, inverted follicular keratosis, large cell acanthoma, lichenoid keratosis, and flat seborrheic keratosis. Although the etiology and pathogenesis of SKs are still largely debatable, several underlying mechanisms and contributing factors have been identified. All subtypes represent benign lesions, and treatment is usually done for cosmetic reasons. Several of the subtypes may act as cutaneous markers for internal malignancy and should be monitored closely for any atypical changes. CONCLUSION: Although all subtypes of SK are benign, their association with other malignant lesions and ability to serve as cutaneous markers of internal malignancy emphasize the importance of correctly identifying all variants.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.582
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.054
GPT teacher head0.320
Teacher spread0.266 · 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 designNot applicable
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

Citations44
Published2008
Admission routes1
Has abstractyes

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