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Record W3017921583 · doi:10.1111/his.14123

Somatic tumour testing establishes that bilateral <i>DICER1</i>‐associated ovarian Sertoli–Leydig cell tumours represent independent primary neoplasms

2020· article· en· W3017921583 on OpenAlexaff
W. Glenn McCluggage, Anne‐Laure Chong, Leanne de Kock, William D. Foulkes

Bibliographic record

VenueHistopathology · 2020
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsMcGill UniversityJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsGermlineGermline mutationContext (archaeology)Somatic cellOvaryMetastasisPathologyGerm cellBiologyMedicineMutationInternal medicineCancerGeneticsGene

Abstract

fetched live from OpenAlex

AIMS: Sertoli-Leydig cell tumours (SLCTs) are rare ovarian neoplasms that are commonly associated with somatic or germline DICER1 mutations, especially when of the moderately or poorly differentiated type. A large majority are unilateral, but bilateral neoplasms have been reported, sometimes in the context of germline DICER1 mutations (DICER1 syndrome). It is currently unknown whether these represent independent neoplasms or metastasis from one ovary to the other and we aimed to elucidate this. METHODS AND RESULTS: We report three cases of bilateral ovarian SLCT (all in patients with DICER1 syndrome) and review all reported cases of bilateral neoplasms. In the three cases (all moderately or poorly differentiated neoplasms), the time interval between the discovery of the tumours in each ovary ranged from 2.7 years to 6 years. In all cases, different DICER1 somatic hotspot mutations within the two tumours provided definitive proof that they represent independent neoplasms; this may be important clinically. Our literature review revealed that, when this information was available, all patients with bilateral SLCT had a germline DICER1 mutation. CONCLUSIONS: Bilateral ovarian SLCTs represent independent rather than metastatic neoplasms, and essentially always occur in the context of DICER1 syndrome.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.040
GPT teacher head0.237
Teacher spread0.196 · 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.

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

Citations12
Published2020
Admission routes1
Has abstractyes

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