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Record W4413140294 · doi:10.1177/12034754251364870

Dermatologic Mimickers of Paget’s Disease of the Breast: A Systematic Review

2025· review· en· W4413140294 on OpenAlexaff
Emily Volfson, Michal Moshkovich, Rebeca Yakubov, Jaycie Dalson, Carly Kirshen

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2025
Typereview
Languageen
FieldMedicine
TopicCancer and Skin Lesions
Canadian institutionsOttawa HospitalUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsMedicineDermatologyBiopsyBreast cancerMEDLINEConfusionDuctal carcinomaCancerRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Mammary Paget disease (MPD) is a rare form of breast cancer that accounts for just 1% to 4% of all cases and is often associated with underlying malignancies such as ductal carcinoma in situ and invasive ductal carcinoma. Its clinical presentation frequently mimics benign dermatologic conditions or malignant melanoma, leading to diagnostic confusion and significant treatment delays. This review explores the diagnostic challenges and patterns of misdiagnosis in MPD, as well as the consequences of delayed recognition. A comprehensive search of Embase and MEDLINE identified 29 studies reporting on 32 cases of MPD, all of which were initially misdiagnosed-most commonly as melanoma (44.4%) and atopic dermatitis (25.0%). The average diagnostic delay was 2.3 years. Most lesions were unilateral (93.8%) and involved the nipple-areolar complex (87.5%). Imaging modalities demonstrated limited sensitivity, reinforcing the importance of early biopsy for timely diagnosis. Surgical intervention was the predominant treatment approach, employed in 75% of cases, and no recurrences were reported during a mean follow-up of 1.3 years. These findings underscore the urgent need for heightened clinical suspicion, earlier tissue sampling, and the development of standardized diagnostic pathways to reduce misdiagnosis and improve outcomes in patients with MPD.

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)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.089
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.333
Teacher spread0.296 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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