MétaCan
Menu
Back to cohort
Record W2727227103 · doi:10.4158/ep161685.cr

Malignant Ovarian Steroid Cell Tumor Causing Severe Hyperandrogenism: Case Report And Review Of The Literature

2017· article· en· W2727227103 on OpenAlexaff
Omalkhaire M. Alshaikh, Stéphane Laframboise, L. Sylvia, Blaise Clarke, Özgür Mete, Shereen Ezzat

Bibliographic record

VenueAACE Clinical Case Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineHyperandrogenismDehydroepiandrosterone sulfateAndrogen ExcessTestosterone (patch)AndrogenHistopathologyOvarian tumorDehydroepiandrosteroneInternal medicinePathologyHormoneOvarian cancerCancerPolycystic ovary

Abstract

fetched live from OpenAlex

Objective: To report a rare case of malignant ovarian steroid cell tumor with androgen excess.Methods: We describe the clinical presentation and management of a malignant ovarian steroid tumor with severe hyperandrogenism in a postmenopausal woman.Results: An 83-year-old patient had a 6-year history of features of androgen excess. Biochemistry confirmed elevated testosterone and dehydroepiandrosterone sulfate; investigations identified a 4.5-cm adnexal mass that was resected, and the histopathology diagnosis was a malignant steroid cell tumor. Eight months later, disease recurrence prompted a second surgical intervention, and a 3.5-cm mass adherent to the right pelvic sidewall was removed. The patient was not able to tolerate mitotane due to severe side effects. Eighteen months later, the disease relapsed with recurrence of clinical and biochemical features of androgen excess.Conclusion: This case illustrates the challenges of diagnosis and management of malignant ovarian steroid cell tumors.Abbreviations: CT computed tomography; DHEAS dehydroepiandrosterone sulfate; GnRH gonadotropin-releasing hormone; MRI magnetic resonance imaging; SCT steroid cell tumor

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.042
GPT teacher head0.359
Teacher spread0.317 · 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 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

Citations7
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAACE Clinical Case ReportsSame topicOvarian cancer diagnosis and treatmentFrench-language works237,207