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Record W3133006460 · doi:10.1016/j.gore.2021.100729

Can TP53 variant negative be high-grade serous ovarian carcinoma? A case series

2021· article· en· W3133006460 on OpenAlexafffund
Lawrence Kasherman, Swati Garg, Naïri Tchrakian, Blaise Clarke, Katherine Karakasis, Raymond H. Kim, Tracy Stockley, Neesha C. Dhani, Amit M. Oza, Stéphanie Lheureux

Bibliographic record

VenueGynecologic Oncology Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
FundersGovernment of OntarioOntario Institute for Cancer Research
KeywordsSerous fluidMedicineSerous carcinomaOvarian cancerOvarian carcinomaSerous ovarian cancerImmunohistochemistryPathologyOvarian carcinomasOncologyCancer researchInternal medicineCancer

Abstract

fetched live from OpenAlex

• TP53 variant negative high-grade serous ovarian cancer is rare and can still show p53 abnormal immunohistochemistry. • Diagnostic and therapeutic considerations include pathologic, molecular and clinical domains. • Genetic reassessment through more comprehensive assays should be considered to ensure no missed rare or complex variants. • Presence of BRCA mutations can occur in TP53 variant high-grade serous ovarian cancer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.296
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2021
Admission routes2
Has abstractyes

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