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MyD88 and TLR4 Expression in Epithelial Ovarian Cancer
Matthew S. Block, Robert A. Vierkant, Peter Rambau, Stacey J. Winham, Philipp Wagner, Nadia Traficante, Aleksandra Tołoczko, Daniel Guimarães Tiezzi, Florin‐Andrei Taran, Hans‐Peter Sinn, Weiva Sieh, Raghwa Sharma, Joseph H. Rothstein, Teresa Ramón y Cajal, Luis Paz‐Ares, Oleg Oszurek, Sandra Oršulić, Roberta B. Ness, Gregg Nelson, Francesmary Modugno, Janusz Menkiszak, Valerie McGuire, Bryan M. McCauley, Marie Mack, Jan Lubiński, Teri A. Longacre, Zheng Li, Jenny Lester, Catherine J. Kennedy, Kimberly R. Kalli, Audrey Jung, Sharon E. Johnatty, Mercedes Jimenez‐Liñan, Allan Jensen, Maria P. Intermaggio, Jillian A. Hung, Esther Herpel, Brenda Y. Hernandez, Andreas D. Hartkopf, Paul R. Harnett, Prafull Ghatage, Jose Maria Garcia-Bueno, Bo Gao, Sián Fereday, Ursula Eilber, Robert P. Edwards, Christiani Bisinoto de Sousa, Jurandyr Moreira de Andrade, Anita Chudecka-Głaz, Georgia Chenevix‐Trench, Alicia Cazorla, Sara Y. Brucker, David D.L. Bowtell, A. Green, Penelope M. Webb, Anna DeFazio, Dorota M. Gertig, S. Moore, K.R. Harrap, T. Sadkowsky, Nirmala Pandeya, M. Malt, A. Mellon, R. Paul Robertson, T. Vanden Bergh, Marsha Jones, P. Mackenzie, J. Maidens, K. Nattress, Yoke-Eng Chiew, Annie Stenlake, Harold C. Sullivan, B. Alexander, P. Ashover, Scott D. Brown, T. Corrish, L. Green, L. M. Jackman, Kaltin Ferguson, Karla Martin, A. Martyn, B. Ranieri, Jeff White, V. Jayde, Pamela 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, Dany 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, Peter Russell, Matthew Links, John J. Grygiel, Jane Hill, Alison H. Brand, Karen Byth, Richard Jaworski, Gerard Wain, B Ward, D. Papadimos, A. Crandon, Michael P. Cummings, K. Horwood, Andreas Obermair, Lewis Perrin, David Wyld, James Nicklin, Martin 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, Deborah Allen, Peter Grant, Simon Hyde, R. Laurie, Melissa Robbie, David Healy, Tom Jobling, T. Manolitsas, J. McNealage, Peter A. W. Rogers, B. Susil, E. Sumithran, Ian Simpson, Kelly‐Anne Phillips, Danny Rischin, Simon A. Fox, Diana 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, Jennifer Alsop, Alice S. Whittemore, Helen Steed, Annette Staebler, Kirsten B. Moysich, Usha Menon, Jennifer M. Koziak, Stefan Kommoss, Susanne K. Kjær, Linda E. Kelemen, Beth Y. Karlan, David G. Huntsman, Estrid Høgdall, Jacek Gronwald, Marc T. Goodman, C. Blake Gilks, María J. García, Peter A. Fasching, Anna de Fazio, Suha Deen, Jenny Chang‐Claude, Francisco José Cândido dos Reis, Ian Campbell, James D. Brenton, Javier Benı́tez, Paul D.P. Pharoah, Martin Köbel, Susan J. Ramus, Ellen L. Goode
Bibliographic record
VenueMayo Clinic Proceedings · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaAlberta Health ServicesBC Cancer AgencyRoyal Alexandra HospitalUniversity of Calgary
FundersMedical Research and Materiel CommandNational Center for Research ResourcesNational Cancer InstituteCancer Research UK Cambridge Institute, University of CambridgeInstituto de Salud Carlos IIICancer Council TasmaniaCancer Council VictoriaNational Center for Advancing Translational SciencesMedical Research CouncilCumming School of Medicine, University of CalgaryCancer Institute, University of PittsburghGraduate School of Public Health, University of PittsburghNational Institutes of HealthUniversitätsklinikum Hamburg-EppendorfUniversitätsklinikum HeidelbergNational Health and Medical Research CouncilUniversidade de São PauloRoyal Adelaide HospitalNottingham University Hospitals NHS TrustCancer Research UKUniversity of SydneyCancer AustraliaUniversity of NottinghamIllawarra Shoalhaven Local Health DistrictBundesministerium für Bildung und ForschungNational Institute for Health and Care ResearchUniversity of PittsburghCalgary Laboratory ServicesUniversity of CambridgeAmerican Cancer SocietyMagee-Womens Research InstituteCancer Council South AustraliaPomorski Uniwersytet Medyczny W SzczecinieDeutsches KrebsforschungszentrumCanadian Institutes of Health ResearchDepartment of Obstetrics, Gynecology and Reproductive Sciences, University of PittsburghCancer Institute NSWKræftens BekæmpelseQIMR Berghofer Medical Research InstitutePeter MacCallum Cancer CentreCedars-Sinai Medical CenterConselho Nacional de Desenvolvimento Científico e TecnológicoUniversity of Western SydneyCancer Council NSWOvarian Cancer Australia
KeywordsMedicineEpithelial ovarian cancerOvarian cancerExpression (computer science)Cancer researchOncologyTLR4GynecologyCancerInternal medicine
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 distilled prediction
Teacher imitationNot 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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 teacher head, not a consensus.
Study designBench or experimental
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
Citations30
Published2018
Admission routes2
Has abstractno