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USCAP 2022 Abstracts: Informatics (977-1017)
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
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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.592
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 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.
The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Citations0
Published2022
Admission routes1
Has abstractno