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Hepcidin-regulating iron metabolism genes and pancreatic ductal adenocarcinoma: a pathway analysis of genome-wide association studies
Sachelly Julián‐Serrano, Fangcheng Yuan, William Wheeler, Beben Benyamin, Mitchell J. Machiela, Alan A. Arslan, Laura E. Beane Freeman, Paige M. Bracci, Eric J. Duell, Mengmeng Du, Steven Gallinger, Graham G. Giles, Phyllis J. Goodman, Charles Kooperberg, Loı̈c Le Marchand, Rachel Ε. Neale, Xiao‐Ou Shu, Stephen K. Van Den Eeden, Kala Visvanathan, Wei Zheng, Demetrius Albanes, Gabriella Andreotti, Eva Ardanáz, Ana Babić, Sonja I. Berndt, Lauren K. Brais, Paul Brennan, Bas Bueno‐de‐Mesquita, Julie E. Buring, Stephen J. Chanock, Erica J. Childs, Charles C. Chung, Eleonóra Fabiánová, Lenka Foretová, Charles S. Fuchs, J. Michael Gaziano, Manuel Gentiluomo, Edward L. Giovannucci, Michael Goggins, Thilo Hackert, Patricia Hartge, Manal M. Hassan, Ivana Holcátová, Elizabeth A. Holly, Rayjean I Hung, Vladimír Janout, Robert C. Kurtz, I‐Min Lee, Núria Malats, David McKean, Roger L. Milne, Christina C. Newton, Ann L. Oberg, Sandra Pérdomo, Ulrike Peters, Miquel Porta, Nathaniel Rothman, Matthias B. Schulze, Howard D. Sesso, Debra T. Silverman, Ian M. Thompson, Jean Wactawski‐Wende, Elisabete Weiderpass, Nicolas Wenstzensen, Emily White, Lynne R. Wilkens, Herbert Yu, Anne Zeleniuch‐Jacquotte, Jun Zhong, Peter Kraft, Dounghui Li, Peter T. Campbell, Gloria M. Petersen, Brian M. Wolpin, Harvey A. Risch, Laufey T. Ámundadóttir, Alison P. Klein, Kai Yu, Rachael Z. Stolzenberg‐Solomon
Bibliographic record
VenueAmerican Journal of Clinical Nutrition · 2021
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research Institute
FundersNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNational Human Genome Research InstituteNational Institute on Drug AbuseAgència de Gestió d'Ajuts Universitaris i de RecercaNational Health and Medical Research CouncilMinisterstvo Zdravotnictví Ceské RepublikyNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteDivision of Cancer Epidemiology and Genetics, National Cancer InstituteMinistero della SaluteBundesministerium für Bildung und ForschungCentre International de Recherche sur le CancerGrantová Agentura České RepublikyGeoffrey Beene FoundationInstituto de Salud Carlos IIICalifornia Department of Public HealthLustgarten FoundationMemorial Sloan-Kettering Cancer CenterJohns Hopkins UniversityNational Institutes of HealthUniversity of CaliforniaU.S. Department of DefenseWorld Health OrganizationU.S. Department of Health and Human Services
KeywordsHepcidinHAMPBiologyFerroportinTransferrin receptorHemochromatosisSingle-nucleotide polymorphismPopulationTransferrinFerritinEndocrinologyInternal medicineGeneticsGeneGenotypeMedicineImmunologyBiochemistry
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)0.006
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 score0.034
Threshold uncertainty score0.662
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
| 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 head0.034
GPT teacher head0.346
Teacher spread0.312 · 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
Citations39
Published2021
Admission routes1
Has abstractno