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Record W4312678234 · doi:10.22514/ejgo.2022.064

Low-grade serous carcinoma with solid growth pattern: an unusual architecture and potential pitfall

2022· article· en· W4312678234 on OpenAlexaff
Elizabeth Arslanian, M. Ruhul Quddus, Linda C. Hanley

Bibliographic record

VenueEuropean Journal of Gynaecological Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineSerous fluidSerous carcinomaPathologyOncologyInternal medicineCancer

Abstract

fetched live from OpenAlex

Low-grade serous carcinoma of the ovary is an uncommon lesion, composing approximately 3% of ovarian neoplasms. It typically arises in association with a serous borderline tumor and is most often at an advanced stage upon diagnosis. Gene mutations in BRAF and KRAS are characteristic. Various histologic architectural patterns are known, such as papillary, micropapillary, inverted micropapillary, glandular and nested. We report a case of low-grade serous carcinoma arising years after a serous borderline tumor and contralateral teratoma; the low-grade serous carcinoma showed two patterns: micropapillary growth and a previously unreported form of solid pattern manifesting as large tumor islands without slit-like spaces. This unusual solid morphology raises the differential diagnosis of high-grade serous carcinoma, which would result in different clinical management. The presence of areas with classic micropapillary architecture, in addition to the absence of high-grade cytonuclear atypia and marked pleomorphism, support the diagnosis of low-grade serous carcinoma. Immunohistochemical stains for p53 and p16 failed to show abnormal patterns characteristic of high-grade serous carcinoma. The patient declined chemotherapy and is on letrozole; she has had recurrent right pleural effusions over six months of follow-up after surgery.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Gynaecological OncologySame topicOvarian cancer diagnosis and treatmentFrench-language works237,207