MétaCan
Menu
Back to cohort
Record W4205384952 · doi:10.3390/curroncol29020037

Case Report of Small Cell Carcinoma of the Ovary, Hypercalcemic Type (Ovarian Rhabdoid Tumor) with SMARCB1 Mutation: A Literature Review of a Rare and Aggressive Condition

2022· review· en· W4205384952 on OpenAlexvenueno aff
Maria Fernanda Evangelista Simões, Alexandre André Balieiro Anastácio da Costa, Tullio Novaes Silva, Lizieux Fernandes, Graziele Bovolim, Giovana Tardin Torrezan, Dirce Maria Carraro, Glauco Baiocchi, Ademir Narcizo Oliveira Menezes, Elizabeth Santana Dos Santos, Louise De Brot

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsSMARCA4MedicineRare diseaseSMARCB1Small-cell carcinomaMutationOvaryDiseaseChemotherapyOvarian carcinomaSomatic cellPathologyCancer researchOncologyInternal medicineOvarian cancerBiologyCancerGeneticsGeneEpigenetics

Abstract

fetched live from OpenAlex

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare and aggressive condition that is associated with the SMARCA4 mutation and has a dismal prognosis. It is generally diagnosed in young women. Here, we report a case of a young woman with SCCOHT harboring a rare molecular finding with a highly aggressive biological behavior. The patient had a somatic SMARCB1 mutation instead of an expected SMARCA4 alteration. Even though the patient was treated with high-dose chemotherapy followed by stem cell transplantation, she evolved with disease progression and died 11 months after her first symptoms appeared. We present a literature review of this rare disease and discuss the findings in the present patient in comparison to expected molecular alterations and options for SCCOHT treatment.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.783
Threshold uncertainty score0.653

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 head0.056
GPT teacher head0.354
Teacher spread0.298 · 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 designNot applicable
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

Citations19
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCurrent OncologySame topicChromatin Remodeling and CancerFrench-language works237,207