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Record W4386321429 · doi:10.2139/ssrn.4555260

The Clinical and Biological Landscape of Constitutional Mismatch Repair Deficiency: An IRRDC Study

2023· preprint· en· W4386321429 on OpenAlexaff
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

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.083
GPT teacher head0.300
Teacher spread0.217 · 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.

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

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