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Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine.
Deirdre K Tobias, Jordi Merino, Abrar Ahmad, Catherine Aiken, Jamie L. Benham, Dhanasekaran Bodhini, Amy L Clark, Kevin Colclough, Rosa Corcoy, Sara J. Cromer, Jamie L. Felton, Pieter Gillard, Véronique Gingras, Romy Gaillard, Eram Ali Haider, Jennifer M. Iklé, Laura M. Jacobsen, Anna R Kahkoska, Jarno L. T. Kettunen, Raymond J. Kreienkamp, Lee-Ling Lim, Jonna M. E. Männistö, Robert Massey, Niamh‐Maire Mclennan, Rachel Miller, J. Most, Rochelle N. Naylor, Bige Özkan, Kashyap Amratlal Patel, SCOTT J. PILLA, Katsiaryna Prystupa, Sridharan Raghavan, MARY R. ROONEY, Martin Schön, Zhila Semnani-Azad, Magdalena Sevilla-Gonzalez, Pernille Svalastoga, Wubet Worku Takele, Claudia Ha-Ting Tam, Amelia S. Wallace, Caroline C. Wang, Jessie J Wong, Jennifer M Yamamoto, Katherine Young, Chloé Amouyal, Mette K. Andersen, Maxine P. Bonham, Mingling Chen, Feifei Cheng, Tinashe Chikowore, Sian C. Chivers, Christoffer Clemmensen, Dana Dabelea, Adem Y. Dawed, Aaron J. Deutsch, Laura T. Dickens, Linda A. DiMeglio, Monika Dudenhöffer-Pfeifer, Carmella Evans-Molina, María Mercè Fernández-Balsells, Hugo Fitipaldi, Stephanie L. Fitzpatrick, Stephen E Gitelman, Jessica A. Grieger, Marta Guasch‐Ferré, Nahal Habibi, Torben F. Hansen, Chuiguo Huang, Heba M Ismail, Benjamin Hoag, Randi K. Johnson, Angus G. Jones, Robert W. Koivula, Aaron Leong, Gloria K. W. Leung, Ingrid M Libman, Kai Liu, S Alice Long, William L. Lowe, Robert W. Morton, Ayesha A. Motala, Suna Onengut-Gumuscu, James S Pankow, Maleesa Pathirana, Sofia Pazmiño, Dianna Perez, J Petrie, CAMILLE E. POWE, Alejandra Quinteros, Rashmi Jain, Debashree Lala Ray, Zeb Saeed, Sarah Kanbour, Sudipa Sarkar, Gabriela S. F. Monaco, Denise M Scholtens, Elizabeth Selvin, Wayne Huey-Herng Sheu, CATE SPEAKE, Andrea K Steck, Norbert Stefan, Julie Støy, Rachael Taylor, Sok Cin Tye, Gebresilasea Gendisha Ukke, Bart Van der Schueren, Camille Vatier, John M. Wentworth, Wesley Hannah, Sara L. White, Gechang Yu, Yingchai Zhang, Shao J. Zhou, Jacques Beltrand, Michel Polak, Ingvild Aukrust, Elisa De Franco, Kristin A. Maloney, Andrew McGovern, Janne Molnes, Mariam Nakabuye, Hugo Pomares-Millan, Cuilin Zhang, Sungyoung Auh, Russell de Souza, Chandra Gruber, Eskedar Getie Mekonnen, Emily Mixter, Diana Sherifali, Robert H. Eckel, John J Nolan, Louis H. Philipson, Rebecca J Brown, Liana K. Billings, Kristen Boyle, Tina Costacou, John M Dennis, Jose C Florez, Anna L. Gloyn, Maria F. Gomez, Peter A. Gottlieb, Siri Atma W. Greeley, Kurt Griffin, Andrew T Hattersley, IRL HIRSCH, Marie-France Hivert, Korey K Hood, Jami L. Josefson, Soo Heon Kwak, Lori M Laffel, Siew S Lim, Ruth JF Loos, Chantal Mathieu, Nestoras Mathioudakis, James B. Meigs, Shivani Misra, Viswanathan Mohan, Rinki Murphy, Richard Oram, Katharine R Owen, Susan E. Ozanne, Pearson ER, Wei Perng, Rodica Pop-Busui, Richard E Pratley, Maria J Redondo, Rebecca M. Reynolds, Robert K Semple, Jennifer L Sherr, Emily K. Sims, Arianne Sweeting, Kimberly K. Vesco, TINA VILSBØLL, Robert Wagner, Stephen S Rich
Bibliographic record
VenueApollo (University of Cambridge) · 2023
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of General Medical SciencesWellcome TrustHong Kong GovernmentNational Heart, Lung, and Blood InstituteTrond Mohn stiftelseSun PharmaAmerican Heart AssociationNovo Nordisk FondenPfizerDiabetesforbundetEuropean Association for the Study of DiabetesAustralian GovernmentLunds UniversitetNorges ForskningsrådNational Health Research InstitutesNovo NordiskInnovation and Technology CommissionMinistero della SaluteAlbert-Ludwigs-Universität FreiburgMoonshot Research and Development ProgramCanadian Institutes of Health ResearchDiabetes UKNational Institute for Health and Care ResearchJohn Templeton FoundationBritish Heart FoundationBoettcher FoundationDiabetes Research ConnectionNational Institutes of HealthBigfoot BiomedicalDexcomAstraZenecaResearch EnglandInsulet CorporationEli Lilly and CompanyImmune Tolerance NetworkMannKind CorporationDoris Duke Charitable FoundationPediatric Endocrine SocietyKU LeuvenHamilton Health SciencesCenter for Duchenne Muscular Dystrophy, University of California, Los AngelesMedical Research CouncilLeona M. and Harry B. Helmsley Charitable TrustU.S. Department of Veterans AffairsDaiichi Sankyo EuropeYale UniversityNational Health and Medical Research CouncilNational Institute of Allergy and Infectious DiseasesSanofiAmerican Diabetes Association
KeywordsPrecision medicineMEDLINECommon cause and special causePresentation (obstetrics)Key (lock)Evidence-based medicineAlternative medicineSystematic review
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)0.168
metaresearch head score (Gemma)0.152
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.168
Threshold uncertainty score0.890
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.168 | 0.152 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.014 | 0.014 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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 head0.119
GPT teacher head0.338
Teacher spread0.220 · 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 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
Published2023
Admission routes1
Has abstractno