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Record W2955057356 · doi:10.1038/s41379-019-0302-0

A combination of the immunohistochemical markers CK7 and SATB2 is highly sensitive and specific for distinguishing primary ovarian mucinous tumors from colorectal and appendiceal metastases

2019· article· en· W2955057356 on OpenAlexafffund
Nicola S. Meagher, Linyuan Wang, Peter Rambau, Maria P. Intermaggio, David G. Huntsman, Lynne R. Wilkens, Mona El‐Bahrawy, Roberta B. Ness, Kunle Odunsi, Helen Steed, Esther Herpel, Michael S. Anglesio, Bonnie Zhang, Neil Lambie, Anthony J. Swerdlow, Jan Lubiński, Robert A. Vierkant, Ellen L. Goode, Usha Menon, Aleksandra Tołoczko‐Grabarek, Oleg Oszurek, Sanela Bilic, Aline Talhouk, Montserrat García‐Closas, Qin Wang, Adeline Tan, Rhonda Farrell, Catherine J. Kennedy, Mercedes Jimenez‐Liñan, Karin Sundfeldt, John Lewis Etter, Janusz Menkiszak, Marc T. Goodman, Paul Klonowski, Yee Leung, Stacey J. Winham, Kirsten B. Moysich, Sabine Behrens, Tomasz Kluz, Robert P. Edwards, Jacek Gronwald, Francesmary Modugno, Brenda Y. Hernandez, Christine Chow, Linda E. Kelemen, Gary L. Keeney, Michael E. Carney, Yanina Natanzon, Gregory Robertson, Raghwa Sharma, Simon A. Gayther, Jennifer Alsop, Hugh Luk, Chloe Karpinskyj, Ian Campbell, Hans‐Peter Sinn, Aleksandra Gentry‐Maharaj, Penny Coulson, Jenny Chang‐Claude, Mitul Shah, Martin Widschwendter, Katrina Tang, Minouk J. Schoemaker, Jennifer M. Koziak, Linda S. Cook, James D. Brenton, Frances Daley, Björg Kristjánsdóttir, Constantina Mateoiu, Melissa C. Larson, Paul R. Harnett, Audrey Jung, Anna DeFazio, Kylie L. Gorringe, Paul D.P. Pharoah, Parham Minoo, Colin J.R. Stewart, Oliver F. Bathe, Xianyong Gui, Paul A. Cohen, Susan J. Ramus, Martin Köbel

Bibliographic record

VenueModern Pathology · 2019
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsAlberta Health ServicesRoyal Alexandra HospitalVancouver General HospitalUniversity of British ColumbiaBC Cancer AgencyFoothills Medical CentreUniversity of Calgary
FundersNational Cancer InstituteNational Institutes of HealthMedical Research CouncilUniversität HeidelbergSwedish Cancer FoundationCancer Research SocietyNSW Ministry of HealthUniversity of New South WalesOvarian Cancer ActionNational Institute for Health and Care ResearchTranslational Cancer Research NetworkNational Health and Medical Research CouncilCancer Research UKNational Center for Research ResourcesCancer Institute NSWMedical Research and Materiel CommandUniversity of Pittsburgh
KeywordsMedicineCDX2PAX8PathologyTissue microarrayMucinous TumorMucinous carcinomaImmunohistochemistryOvarian tumorOvarian cancerAdenocarcinomaInternal medicineCancerBiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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 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

Citations99
Published2019
Admission routes2
Has abstractno

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