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Record W4220742071 · doi:10.1038/s41374-022-00759-x

USCAP 2022 Abstracts: Informatics (977-1017)

2022· article· en· W4220742071 on OpenAlexaff
Rhonda Yantiss Chair, Kristin Jensen Chair, Cme Subcommittee, Laura C. Collins, Yuri Fedoriw, Ilan Weinreb, Carla Chair, Adebowale Adeniran, Kimberly H. Allison, Sarah Dry, William C. Faquin, Karen Fritchie, Jennifer Gordetsky, Levon Katsakhyan, Pathologist-In-Training Melinda, J Lerwill, M Beatriz, Susana Lopes, Julia Naso, Pathologist-In-Training Liron, Pantanowitz Carlos, Parra-Herran Rajiv, Mishal Patel, Matt Quick, David J. Schaeffer, Lynette M. Sholl, Olga K. Weinberg, Maria Westerhoff, Benjamin Adam, Oyedele Adeyi, Mariam P. Alexander, Daniela Allende, Catalina Amador, Vijayalakshmi Ananthanarayanan, Tatjana Antic, Manju Aron, Roberto Barrios, Gregory Bean Govind, Bhagat Luis, Zabala Blanco, Michael Bonert, Alain Borczuk, Tamar Brandler, Eric Burks, Kelly J. Butnor, Sarah Calkins, Weibiao Cao, Cao Barbara, Ann Centeno, Joanna Sy, Chan Kung-Chao, Chang Hao, Chen Chen, Yunn‐Yi Chen, Sarah Chiang, Soo‐Jin Cho, Shefali Chopra, Nicole A. Cipriani, Cecilia Clement, Claudiu Cotta, Jennifer Cotter, Sonika Dahiya, Elizabeth G. Demicco, Katie Dennis, Jasreman Dhillon, Anand S. Dighe, Bojana Djordjevic, Michelle R. Downes, Charles G. Eberhart, Andrew Evans, Fang Fan, Julie C. Fanburg–Smith, Gelareh Farshid, Michael Feely, Susan Fineberg, Dennis Firchau, Gregory A. Fishbein, Agnes B. Fogo, Andrew L. Folpe, Danielle Fortuna, Billie Fyfe-Kirschner, Zeina Ghorab, Giovanna A. Giannico, Anthony J. Gill, Tamar Giorgadze, Alessio Giubellino, Carolyn Glass, Carmen Gomez‐Fernandez, Shunyou Gong, Purva Gopal, Abha Goyal, Christopher Griffith, Ian S. Hagemann, Gillian Leigh, Hale Suntrea, T Hammer, Malini Harigopal, Kammi Henriksen, Jonas J. Heymann, Carlo Hojilla, Aaron R. Huber, Jabed Iqbal, Shilpa Jain, Vickie Y. Jo, Ivy John, Dan Jones, Ridas Juskevicius, Meghan Kapp, Nora Katabi, Francesca Khani, Joseph D. Khoury, Benjamin R. Kipp, Veronica Klepeis, Christian A. Kunder, Stefano La, Rosa Stephen, Maria Marcella Laganà, Keith Lai, Goo Lee, Michael J. Lee, Vasiliki Leventaki, Madelyn Lew, Faqian Li, Ying Li, Chieh‐Yu Lin, Mikhail Lisovsky, Lesley Lomo, Fang‐I Lu, Ma Varsha, Manucha Rachel, Angelica Mariani, Brock Aaron, Martin David, S Mcclintock, Anne M. Mills, Sangjeong Ahn, Cristina Eunbee Cho, Yeojin Jeong, Ji-Eon Kim, Jonghyun Lee, Namkug Kim, Jiyoon Jung, Ju Yeon Pyo, Ji-Sun Song, Woon Yong Jung, Yoo Jin Lee, Min Kyoung, David Beyer, Etienne Mahé, Leslee Phillips, Heather Sereda, Susan Nahirniak, Bindu Challa, David A. Kellough, Swati Satturwar, Giovanni Lujan, Wendy L. Frankel, Anil V. Parwani, Shaoli Sun, Zaibo Li, Zachary L. Chelsky, Lester J. Layfield, Katie Wilkinson, Richard Hammer, Yaswitha Jampani

Bibliographic record

VenueLaboratory Investigation · 2022
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsAlberta Health ServicesUniversity of AlbertaUniversity of CalgaryAlberta Hospital EdmontonUniversity of Alberta Hospital
FundersFoundation Medicine
KeywordsInformaticsMedicineComputer scienceLibrary scienceEngineeringElectrical engineering

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.879
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0020.001
Scholarly communication0.0060.002
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.8790.796

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 head0.009
GPT teacher head0.253
Teacher spread0.243 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractno

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