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Histologic Features of Melanocytic Nevi in Older Patients

2005· article· en· W1960185276 on OpenAlexaff
Michal Martinka

Bibliographic record

VenueJournal of Cutaneous Pathology · 2005
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsCalgary Laboratory ServicesUniversity of British Columbia
Fundersnot available
KeywordsMedicineDysplastic nevusNevusDermatologyMelanocytic nevusMelanomaAtypiaLentigo malignaPathologySuperficial spreading melanomaHistology

Abstract

fetched live from OpenAlex

The histology of melanocytic nevi in elderly patients often differs from nevi in younger adults. Our hypothesis was that nevi in patients 3 60 yrs would demonstrate histologic patterns different from nevi in younger adults. Biopsies of nevi (n = 214) from 172 patients 3 60 yrs (mean age 69 ± 7 yrs) were examined by three dermatopathologists and a consensus diagnosis was rendered. Control specimens (n = 82) from 58 patients 20–40 yrs (mean age 31 ± 5 yrs) were evaluated. Compound melanocytic nevi were commoner in younger patients (37%) versus older patients (12%)(p < .0001). Junctional melanocytic nevi were more frequently diagnosed in older patients (10% versus 3%; p = .027) and a lentiginous, often heavily pigmented, growth pattern was common (12% of nevi) vs control group (3%; p = .028). Atypical (“dysplastic”) nevi had a similar frequency at all ages, however 12/214 nevi in elderly patients (6%) exhibited marked atypia with features suggesting melanoma in‐situ. We conclude that benign junctional nevi are relatively common in elderly patients and that a lentiginous, heavily pigmented growth pattern, typically associated with younger patients, is often seen in both junctional and compound nevi in this older age group. This pattern must be differentiated from dysplastic nevus and melanoma in‐situ, which they may clinically resemble.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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 designOther design
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

Citations0
Published2005
Admission routes1
Has abstractyes

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