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Refined cut-off for TP53 immunohistochemistry improves prediction of TP53 mutation status in ovarian mucinous tumors: implications for outcome analyses
Eun Young Kang, Dane Cheasley, Cécile Le Page, Matthew J. Wakefield, Michelle da Cunha Torres, Simone M. Rowley, Carolina Salazar, Zhongyue Xing, Prue E. Allan, David D.L. Bowtell, Anne‐Marie Mes‐Masson, Diane Provencher, Kurosh Rahimi, Linda E. Kelemen, Peter A. Fasching, Jennifer A. Doherty, Marc T. Goodman, Ellen L. Goode, Suha Deen, Paul D.P. Pharoah, James D. Brenton, Weiva Sieh, Constantina Mateoiu, Karin Sundfeldt, Linda S. Cook, Nhu D. Le, Michael S. Anglesio, C. Blake Gilks, David G. Huntsman, Catherine J. Kennedy, Nadia Traficante, Georgia Chenevix‐Trench, A. Green, Penelope M. Webb, Anna DeFazio, Dorota M. Gertig, Sián Fereday, Suzanne Moore, Jillian A. Hung, K.R. Harrap, T. Sadkowsky, Nirmala Pandeya, M. Malt, A. Mellon, R. Paul Robertson, T. Vanden Bergh, Michael E. Jones, P. Mackenzie, J. Maidens, K. Nattress, Yoke-Eng Chiew, Annie Stenlake, Harold C. Sullivan, Barbara D. Alexander, P. Ashover, Stephen M. Brown, T. Corrish, L. Green, L. M. Jackman, Kaltin Ferguson, Karla Martin, A. Martyn, B. Ranieri, Jeff White, V. Jayde, Pam Mamers, Leanne Bowes, Laura Galletta, Daniel A. Giles, Joy Hendley, Kathryn Alsop, Tannin A. Schmidt, H. Shirley, C. Ball, Christian D. Young, S. Viduka, Hoa Tran, Sanela Bilic, Lydia Glavinas, Julia Brooks, R. Stuart‐Harris, Fred Kirsten, J Rutovitz, P. Clingan, Akisha Glasgow, Anthony Proietto, Stephen Braye, Geoffrey Otton, Jenny Shannon, Tony Bonaventura, Jocelyn M. Stewart, Stephen Begbie, Michael Friedländer, Debra Bell, Sally Baron‐Hay, A. Ferrier, G. Gard, David Nevell, Nick Pavlakis, Susan Valmadre, B. Young, C. Camaris, R. Crouch, L. Edwards, Neville F. Hacker, Donald E. Marsden, Gregory Robertson, Philip Beale, Jane Beith, J. Carter, C. Dalrymple, R. Houghton, Prudence A. Russell, Matthew Links, John J. Grygiel, Jane Hill, Alison H. Brand, Karen Byth, Richard Jaworski, Paul R. Harnett, R. Sharma, Gerard Wain, B. Ward, D. Papadimos, A. Crandon, Michael P. Cummings, K. Horwood, Andreas Obermair, Lewis Perrin, David Wyld, James Nicklin, Marcus Davy, Martin K. Oehler, Cathrine Hall, Tom Dodd, Timothy M. Healy, Keir Pittman, D C Henderson, J. Miller, J. Pierdes, Penny Blomfield, D. Challis, Rachel McIntosh, Alyssa Parker, Robert Brown, Robert Rome, Digby Allen, Peter Grant, Simon Hyde, R. Laurie, Melissa Robbie, D. Healy, Tom Jobling, T. Manolitsas, J. McNealage, Peter A. W. Rogers, B. Susil, E. Sumithran, Ian Simpson, Kelly‐Anne Phillips, Danny Rischin, Stephen B. Fox, Daryl Johnson, Stephen Lade, Maurice B. Loughrey, N. O’Callaghan, William K. Murray, Paul Waring, Virginia Billson, Jan Pyman, Deborah Neesham, Michael Quinn, Craig Underhill, Rachel Bell, L. F. Ng, Robert Blum, Vinod Ganju, Ian Hammond, Yee Leung, Anthony J. McCartney, Martin Buck, I. Haviv, D. Purdie, David C. Whiteman, Nikolajs Zeps, Scott H. Kaufmann, Michael Churchman, Charlie Gourley, Andrew N. Stephens, Nicola S. Meagher, Susan J. Ramus, Yoland Antill, Ian Campbell, Clare L. Scott, Martin Köbel, Kylie L. Gorringe, Georgina L. Ryland, Sumitra Ananda, George Au‐Yeung, Maret Böhm, Michael Christie, Yoke-Eng Chiew, Rhiannon Dudley, Nicole Fairweather, Alison Hadley, Gwo‐Yaw Ho, Sally M. Hunter, Kimberly R. Kalli, Orla McNally, Jessica N. McAlpine, Linda Mileshkin, Goli Samimi
Bibliographic record
VenueModern Pathology · 2020
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaCentre Hospitalier de l’Université de MontréalUniversity of Calgary
FundersMedical Research and Materiel CommandNational Cancer InstituteCancer Council TasmaniaCancer Council VictoriaTerry Fox Research InstituteCancer Institute NSWCanadian Institutes of Health ResearchNational Institutes of HealthBC Cancer FoundationCancer Council South AustraliaUniversité de MontréalCancer AustraliaNational Institute for Health and Care ResearchNational Health and Medical Research CouncilUniversity of CambridgeMinnesota Ovarian Cancer AllianceHuntsman Cancer FoundationCancer Research UKMichael Smith Health Research BCMayo Foundation for Medical Education and ResearchCancer Council NSWOvarian Cancer AustraliaMarshfield Clinic Research FoundationVictorian Cancer AgencyFred C. and Katherine B. Andersen FoundationMedical Research CouncilPeter MacCallum FoundationCancer Research SocietySwedish Cancer Foundation
KeywordsTissue microarrayImmunohistochemistryConcordancePathologyMedicineOvarian cancerOncologyCohortAnatomical pathologyInternal medicineBiologyCancer
Abstract
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 imitationNot 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)0.007
metaresearch head score (Gemma)0.015
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.036
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 head0.088
GPT teacher head0.377
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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
Citations41
Published2020
Admission routes2
Has abstractno