MétaCan
Menu
Back to cohort
Record W4416087421 · doi:10.1177/10668969251384328

Basal Cell Carcinoma With Matrical Differentiation: A Case Report and Literature Review

2025· article· en· W4416087421 on OpenAlexaff
Samantha Keow, Joshua Del Papa, Matthew J. Cecchini, Anurag Sharma

Bibliographic record

VenueInternational Journal of Surgical Pathology · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer and Skin Lesions
Canadian institutionsWestern UniversityLondon Health Sciences CentreMcMaster University
Fundersnot available
KeywordsBasal cell carcinomaDifferential diagnosisBiopsyKeratinBasal cellHead and neckNodule (geology)Basal (medicine)

Abstract

fetched live from OpenAlex

Basal cell carcinoma (BCC) with matrical differentiation is an extremely rare subtype of basal cell carcinoma. We present an example of BCC with matrical differentiation and review the relevant literature. A 75-year-old man presented with a rapidly enlarging nodule on his forehead, with the clinical diagnosis of squamous cell carcinoma. Given atypical matrical proliferation on initial biopsy, there was concern for pilomatrix carcinoma. However, a repeat biopsy confirmed a diagnosis of BCC with matrical differentiation. Immunohistochemically, the BCC lobules expressed BCL2 and BER-EP4, while the areas of matrical differentiation showed nuclear β-catenin expression. The tumor cells were negative for keratin 7. BCC with matrical differentiation predominantly affects men, with a mean age of 70 years. Most tumors occur in the head and neck area, and lesions are often slow-growing or asymptomatic. Differential diagnosis includes pilomatrix carcinoma. Outcomes are generally favorable following surgical excision, although 2 instances of lymph node metastasis have been reported.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.007
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.009
GPT teacher head0.305
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Surgical PathologySame topicCancer and Skin LesionsFrench-language works237,207