← Back to cohort
The Clinical and Biological Landscape of Constitutional Mismatch Repair Deficiency: An IRRDC Study
Ayse B. Ercan, Melyssa Aronson, Nicholas R. Fernandez, Chang Yuan, Adrian Levine, Amy Zhihui Liu, Logine Negm, Melissa Edwards, Vanessa Bianchi, Lucie Stengs, Brian K. Chung, Abeer Al-Battashi, Agnes Reschke, Alex Lion, Alia Ahmad, Álvaro Lassaletta, Alyssa Reddy, Amir Fadhil Al‐Darraji, Amish C. Shah, An Van Damme, Anne Bendel, Aqeela Rashid, Ashley Margol, Bethany L. Kelly, Bojana Pencheva, Brandie Heald, Brianna Lemieux Anglin, Bruce Crooks, Carl Koschmann, Catherine Gilpin, Christopher C. Porter, David Gass, David Samuel, David S. Ziegler, Deborah T. Blumenthal, Dennis John Kuo, Dima Hamideh, Donald Basel, Dong Anh Khuong Quang, Duncan Stearns, Enrico Opocher, Fernando Carcellar, Hagit Baris Feldman, Helen Toledano, Ira Winer, Isabelle Scheers, Ivana Fedoráková, Jack Su, Jaime Vengoechea Barrios, Jaroslav Štěrba, Jeffrey Knipstein, Jordan R. Hansford, Julieta Rita Gonzales-Santos, Kanika Bhatia, Kevin Bielamowicz, Khurram Minhas, Kim E. Nichols, Kristina A. Cole, Lynette S. Penney, Magnus Aasved Hjort, Magnus Sabel, Maria João Gil‐da‐Costa, Matthew J. Murray, Matthew A. Miller, Maude L. Blundell, Maura Massimino, Maysa Al‐Hussaini, Mazin Faisal Al‐Jadiry, Melanie Comito, Michael Osborn, Michael P. Link, Michal Zápotocký, Mithra Ghalibafian, Najma Shaheen, Naureen Mushtaq, Nicolas Waespe, Nobuko Hijiya, Noemi Fuentes Bolanos, O Hasan Ahmad, Omar Chamdine, Paromita Roy, Pavel N. Pichurin, Per Nymen, Rachel Pearlman, Rebecca C. Auer, Reghu K. Sukumaran, Rejin Kebudi, Rina Dvir, Robert M. Raphael, Ronit Elhasid, Rose B. McGee, Rose Chami, Ryan Noss, Ryuma Tanaka, Salmo Raskin, Santanu Sen, Scott Lindhorst, Sebastian Perreault, Shani Caspi, Shazia Riaz, Shlomi Constantini, Sophie Albert, Stanley Chaleff, Stefan Bielack, Stefano Chiaravalli, Stuart Cramer, Sumita Roy, Suzanne Cahn, Suzanne Penna, Syed Ahmer Hamid, Tariq Ghafoor, Uzma Imam, Valérie Larouche, Vanan MagimairajanIssai, William D. Foulkes, Yi Yen Lee, Yosef E. Maruvka, Mary‐Louise C. Greer, Carol Durno, Adam Shlien, Birgit Ertl‐Wagner, Anita Villani, David Malkin, Cynthia Hawkins, Éric Bouffet, Anirban Das, Uri Tabori
Bibliographic record
VenueSSRN Electronic Journal · 2023
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsUniversité LavalCentre Hospitalier Universitaire Sainte-JustineCancerCare ManitobaHealth Sciences CentreOttawa HospitalJewish General HospitalUniversity of OttawaChildren's Hospital of Eastern OntarioCentre hospitalier de l'Université LavalUniversity of TorontoSt Joseph's Health CareHospital for Sick Children
Fundersnot available
KeywordsMedicine
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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.965
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 head0.083
GPT teacher head0.300
Teacher spread0.217 · 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 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
Citations1
Published2023
Admission routes1
Has abstractno