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Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk
Eileen Dareng, Jonathan P. Tyrer, Daniel R. Barnes, Michelle R. Jones, Xin Yang, Katja K.H. Aben, Muriel A. Adank, Simona Agata, Irene L. Andrulis, Hoda Anton‐Culver, Natalia Antonenkova, Gerasimos Aravantinos, Banu Arun, Annelie Augustinsson, Judith Balmañà, Elisa V. Bandera, Rósa B. Barkardóttir, Matthias W. Beckmann, Alicia Beeghly‐Fadiel, Javier Benı́tez, Marina Bermisheva, Marcus Q. Bernardini, Line Bjørge, Amanda Black, Natalia Bogdanova, Bernardo Bonanni, Åke Borg, James D. Brenton, Agnieszka Budziłowska, Ralf Bützow, Saundra S. Buys, Hui Cai, Maria A. Caligo, Ian Campbell, Rikki Cannioto, Hayley Cassingham, Jenny Chang‐Claude, Stephen J. Chanock, Kexin Chen, Yoke-Eng Chiew, Wendy K. Chung, Kathleen Claes, Sarah V. Colonna, Noura Mebirouk, Christoph Engel, Rita K. Schmutzler, Daniel Barrowdale, E. Davies, Linda S. Cook, Fanny Dao, Joe Dennis, Peter Devilee, Orland Dı́ez, Yuan Chun Ding, Jennifer A. Doherty, Susan M. Domchek, Andreas du Bois, Heather Eliassen, D. Gareth Evans, James M. Flanagan, Renée T. Fortner, Eva Macháčková, Patricia A. Ganz, Judy E. Garber, Graham G. Giles, Andrew K. Godwin, Marc T. Goodman, Mark H. Greene, Christopher A. Haiman, Ute Hamann, Thomas van Overeem Hansen, Holly R. Harris, Florian Heitz, Estrid Høgdall, Claus Høgdall, John L. Hopper, Chad D. Huff, Peter J. Hulick, Evgeny N. Imyanitov, Georgia Chenevix‐Trench, Annemieke H. van der Hout, Claudine Isaacs, Anna Jakubowska, Paul A. James, Allan Jensen, Oskar T. Johannsson, Esther M. John, Michael E. Jones, Beth Y. Karlan, Anthony Karnezis, Linda E. Kelemen, Э. К. Хуснутдинова, Lambertus A. Kiemeney, Byoung‐Gie Kim, Susanne K. Kjær, Ian K. Komenaka, Jolanta Kupryjańczyk, Ava Kwong, Diether Lambrechts, Melissa C. Larson, Conxi Lázaro, Nhu D. Le, Goska Leslie, Jenny Lester, Fabienne Lesueur, Douglas A. Levine, Jingmei Li, Jennifer T. Loud, Jan Lubiński, Siranoush Manoukian, Jeffrey R. Marks, Taymaa May, Lesley McGuffog, Usha Menon, Austin Miller, Roger L. Milne, Francesmary Modugno, Elizabeth Munro, Katherine L. Nathanson, Heli Nevanlinna, Joanne Ngeow, Henriette Roed Nielsen, Finn Cilius Nielsen, Liene Ņikitina-Zaķe, Kunle Odunsi, Edith Olah, Siel Olbrecht, Olufunmilayo I. Olopade, Sara H. Olson, Håkan Olsson, Ana Osório, Sue K. Park, Michael T. Parsons, Harsha Pathak, Inge Søkilde Pedersen, Ana Peixoto, Tanja Pejović, Pedro Perez-Segura, Beth N. Peshkin, Paolo Peterlongo, Anna Piskorz, Darya Prokofyeva, Paolo Radice, Johanna Rantala, Marjorie J. Riggan, Cristina Rodríguez‐Antona, Eric A. Ross, Mary Anne Rossing, Ingo B. Runnebaum, Dale P. Sandler, Marta Santamariña, Penny Soucy, Veronica Wendy Setiawan, Weiva Sieh, Jacques Simard, Christian F. Singer, Honglin Song, Melissa C. Southey, Helen Steed, Dominique Stoppa‐Lyonnet, Rebecca Sutphen, Anthony J. Swerdlow, Yen Y. Tan, Manuel R. Teixeira, Kathryn L. Terry, Mary Beth Terry, Jenny Chang‐Claude, Joe Dennis, Albina N. Minlikeeva, Liv Cecilie Vestrheim Thomsen, Linda Titus, Britton Trabert, Ruth C. Travis, Shelley S. Tworoger, Ellen Valen, Anne M. van Altena, Els Van Nieuwenhuysen, Digna Velez Edwards, Robert A. Vierkant, Penelope M. Webb, Clarice R. Weinberg, Nicolas Wentzensen, Alicja Wolk, Yin Ling Woo, Anna H. Wu, Drakoulis Yannoukakos, Wei Zheng, Anna DeFazio, Susan J. Ramus, Celeste Leigh Pearce, Álvaro N.A. Monteiro, Julie M. Cunningham, Ellen L. Goode, Joellen M. Schildkraut, Andrew Berchuck, Simon A. Gayther, Paul D.P. Pharoah, Xin Yang, Mads Thomassen, Darcy L. Thull, Diana Torres, Nadine Tung, Elizabeth J. van Rensburg, Ana Vega, Barbara Wappenschmidt, Jeffrey N. Weitzel, Katia Zavaglia, Kristin K. Zorn, Antonis C. Antoniou, Zdeněk Kleibl, Douglas F. Easton
Bibliographic record
VenueEuropean Journal of Human Genetics · 2022
Typeerratum
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsMcGill UniversityUniversité LavalRoyal Alexandra HospitalPublic Health OntarioUniversity of British ColumbiaAlberta Health ServicesCentre hospitalier universitaire de QuébecMount Sinai HospitalLunenfeld-Tanenbaum Research InstituteVancouver General HospitalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersNational Institute of General Medical SciencesNational Cancer InstituteMedical Research Council
KeywordsOvarian cancerPolygenic risk scoreComputational biologyBiologyBioinformaticsMedicineOncologyGeneticsCancerGeneSingle-nucleotide polymorphismGenotype
Abstract
Correction to: European Journal of Human Genetics (2021) 30:349-362 https://doi.org/10.1038/s41431-021-00987-7 , published online 14 January 2022
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 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)0.010
metaresearch head score (Gemma)0.190
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.238
Threshold uncertainty score0.796
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.190 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.238 | 0.056 |
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 head0.104
GPT teacher head0.358
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreOther
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
Citations4
Published2022
Admission routes1
Has abstractyes