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Record W4414997549 · doi:10.1111/his.70018

Molecular profile of atypical Leydig cell tumours

2025· article· en· W4414997549 on OpenAlexaff
Muhammad Choudhry, Diogo Caires, Yaser Gamallat, Aslı Yilmaz, Fadi Brimo, Bob Argiropoulos, Tarek A. Bismar

Bibliographic record

VenueHistopathology · 2025
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsMcGill University Health CentreUniversity of Calgary
Fundersnot available
KeywordsGenome instabilityMetastasisImmunohistochemistryMalignant transformationMicrosatellite instabilityLeydig cell

Abstract

fetched live from OpenAlex

AIMS: To investigate histological and copy number variations (CNVs) in Leydig cell tumours (LCTs) of the testis. Although usually benign, a small minority of cases can be associated with a poor prognosis and metastasis. METHODS: We performed whole copy number analysis to compare the genomic profile of atypical (defined by the presence of any atypical features) versus benign LCTs. Our sample consisted of one malignant (with biopsy-proven metastasis), five atypical and five benign cases. RESULTS: We found increased genomic instability in the malignant tumour and within two out of five (40%) atypical cases. One benign case revealed a likely pathogenic mutation in the neurofibromatosis type 2 gene, but all benign cases lacked genomic instability. Apart from the malignant case (which had metastatic spread to the scrotal skin), all remaining atypical cases did not reveal evidence of recurrence or metastatic spread. CONCLUSION: CNVs by themselves are not sufficient to discriminate between cases that are benign versus those with malignant potential, without the use of histomorphological parameters. Genomic instability was only detected in the malignant and atypical cases, and not in any of the benign tumours. Thus, genomic instability may represent an early step in malignant progression. The presence of metastasis remains the only malignant criterion for LCTs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.258
Teacher spread0.252 · 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 designObservational
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

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