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Common variants at 10 genomic loci influence hemoglobin A1C levels via glycemic and nonglycemic pathways (Diabetes (2010) 59, (3229-3239))
Nicole Soranzo, Serena Sanna, Eleanor Wheeler, Christian Gieger, Dörte Radke, Josée Dupuis, Nabila Bouatia‐Naji, Claudia Langenberg, Inga Prokopenko, Man Li, Manjinder S. Sandhu, Matthew M. Heeney, Joseph M. Devaney, Muredach P. Reilly, Sally L. Ricketts, Alexandre F.R. Stewart, Benjamin F. Voight, Christina Willenborg, Benjamin J. Wright, David Altshuler, Dan E. Arking, Beverley Balkau, Daniel R. Barnes, Eric Boerwinkle, Bernhard Böhm, Amélie Bonnefond, Lori L. Bonnycastle, Dorret I. Boomsma, Stefan R. Bornstein, Yvonne Böttcher, Suzannah Bumpstead, Mary Susan Burnett‐Miller, Harry Campbell, Antonio Cao, John C. Chambers, Robert S. Clark, Francis S. Collins, Josef Coresh, Eco J. C. de Geus, Mariano Dei, Panos Deloukas, Angela Döring, Josephine M. Egan, Roberto Elosúa, Luigi Ferrucci, Nita G. Forouhi, Caroline S. Fox, Christopher Franklin, Maria Grazia Franzosi, Sophie Gallina, Anuj Goel, J. Graessler, Harald Grallert, Andreas Greinacher, David Hadley, Alistair S. Hall, Anders Hamsten, Caroline Hayward, Simon Heath, Christian Herder, Georg Homuth, Jouke‐Jan Hottenga, Rachel Hunter‐Merrill, Thomas Illig, Anne Jackson, Antti Jula, Marcus E. Kleber, Christopher W. Knouff, Augustine Kong, Jaspal S. Kooner, Anna Köttgen, Péter Kovács, Knut Krohn, Brigitte Kühnel, Johanna Kuusisto, Markku Laakso, Mark Lathrop, Cécile Lecœur, Mingyao Li, Ruth J. F. Loos, Jian’an Luan, Valeriya Lyssenko, Reedik Mägi, Patrik K. E. Magnusson, Anders Mälarstig, Massimo Mangino, Nicholas G. Martin, Winfried März, Wendy L. McArdle, Ruth McPherson, Christa Meisinger, Thomas Meitinger, Olle Melander, Karen L. Mohlke, Vincent Mooser, Mario A. Morken, Narisu Narisu, David M. Nathan, Matthias Nauck, Chris O’Donnell, Konrad Oexle, Nazario Olla, James S. Pankow, Felicity Payne, John F. Peden, Nancy L. Pedersen, Leena Peltonen, Markus Perola, Ozren Polašek, Eleonora Porcu, Daniel J. Rader, Wolfgang Rathmann, Samuli Ripatti, Ghislain Rocheleau, Michael Roden, Igor Rudan, Veikko Salomaa, Richa Saxena, David Schlessinger, Heribert Schunkert, Peter Schwarz, Udo Seedorf, Elizabeth Selvin, Manuel Serrano‐Ríos, Peter Shrader, Angela Silveira, David S. Siscovick, Kjioung Song, Timothy D. Spector, Kāri Stefánsson, Valgerður Steinthórsdóttir, David P. Strachan, Rona J. Strawbridge, Michael Stümvoll, Ida Surakka, Amy J. Swift, Toshiko Tanaka, Alexander Teumer, Guðmar Þorleifsson, Unnur Þorsteinsdóttir, Anke Tönjes, Gianluca Usala, Véronique Vitart, Henry Völzke, Henri Wallaschofski, Dawn Waterworth, Hugh Watkins, H.‐Erich Wichmann, Sarah H. Wild, Gonneke Willemsen, Gordon H. Williams, James F. Wilson, Juliane Winkelmann, Alan F. Wright, Katja K.H. Aben, Wei Zhao, Stephen E. Epstein, Jeanette Erdmann, Håkon Håkonarson, Sekar Kathiresan, Kay‐Tee Khaw, Robert Roberts, Nilesh J. Samani, Mark D. Fleming, Robert Sladek, Gonçalo R. Abecasis, Michael Boehnke, Philippe Froguel, Leif Groop, Mark I. McCarthy, W.H. Linda Kao, José C. Florez, Manuela Uda, Nicholas J. Wareham, Inês Barroso, James B. Meigs
Bibliographic record
VenueOxford University Research Archive (ORA) (University of Oxford) · 2011
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGlycemicDiabetes mellitusInternal medicineGeneticsBiologyMedicineEndocrinologyBioinformatics
DOInot available
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 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)0.001
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 score0.009
Threshold uncertainty score0.030
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 head0.041
GPT teacher head0.238
Teacher spread0.197 · 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 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
Citations24
Published2011
Admission routes1
Has abstractno