The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Expression-based genome-wide association study links the receptor CD44 in adipose tissue with type 2 diabetes
Open accessAbstract
Type 2 diabetes (T2D) is a complex, polygenic disease affecting nearly 300 million people worldwide. T2D is primarily characterized by insulin resistance, and growing evidence has indicated the causative link between adipose tissue inflammation and the development of insulin resistance. Genetic association studies have successfully revealed a number of important genes consistently associated with T2D to date. However, these robust T2D-associated genes do not fully elucidate the mechanisms underlying the development and progression of the disease. Here, we report an alternative approach, gene expression-based genome-wide association study (eGWAS): searching for genes repeatedly implicated in functional microarray experiments (often publicly available). We performed an eGWAS across 130 independent experiments (totally 1,175 T2D case-control microarrays) to find additional genes implicated in the molecular pathogenesis of T2D and identified the immune-cell receptor CD44 as our top candidate ( P = 8.5 × 10 −20 ). We found CD44 deficiency in a diabetic mouse model ameliorates insulin resistance and adipose tissue inflammation and also found that anti-CD44 antibody treatment decreases blood glucose levels and adipose tissue macrophage accumulation in a high-fat, diet-fed mouse model. Further, in humans, we observed CD44 is expressed in inflammatory cells in obese adipose tissue and discovered serum CD44 levels were positively correlated with insulin resistance and glycemic control. CD44 likely plays a causative role in the development of adipose tissue inflammation and insulin resistance in rodents and humans. Genes repeatedly implicated in publicly available experimental data may have unique functionally important roles in T2D and other complex diseases.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“CD44, the main hyaluronan cell surface receptor is associated with T2DM, as shown by expression-based genome-wide association studies.”
From The Vasculature in Prediabetes (2018) · cites this paper as [176]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280883 · Cited by 31,502
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Inflammation and metabolic disorders
Hotamisligil, G. S. (2006) · Nature · doi:10.1038/nature05485 · Cited by 9,199
Inflammation and insulin resistance
Shoelson, S. E. (2006) · Journal of Clinical Investigation · doi:10.1172/jci29069 · Cited by 4,769
A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993
Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I. W., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., et al. (2007) · Science · doi:10.1126/science.1142358 · Cited by 2,843
A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants
Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., et al. (2007) · Science · doi:10.1126/science.1142382 · Cited by 2,753
Nishimura, S., Manabe, I., Nagasaki, M., Eto, K., Yamashita, H., Ohsugi, M., Otsu, M., Hara, K., Ueki, K., Sugiura, S., et al. (2009) · Nature Medicine · doi:10.1038/nm.1964 · Cited by 2,230
Self-citation
Cited by
Sorted by citations
Items 101–170 of 170
Biomedical Informatics Methods for Personalized Medicine and Participatory Health
Martin-Sanchez, F. J., López–Campos, G., & Gray, K. M. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-401678-1.00011-7 · Cited by 12
Inhibition of hyaluronan synthesis prevents β-cell loss in obesity-associated type 2 diabetes
Nagy, N., Kaber, G., Sunkari, V. G., Marshall, P. L., Hargil, A., Kuipers, H. F., Ishak, H. D., Bogdani, M., Hull, R. L., Grandoch, M., et al. (2023) · Matrix Biology · doi:10.1016/j.matbio.2023.09.003 · Cited by 13
Tagawa, H., Irie, J., Itoh, A., Kusumoto, Y., Kato, M., Kobayashi, N., Tanaka, K., Morinaga, R., Fujita, M., Nakajima, Y., et al. (2015) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2015.04.025 · Cited by 12
Self-citationVerHague, M., Albright, J., Barron, K. A., Kim, M., & Bennett, B. J. (2022) · Biology of Sex Differences · doi:10.1186/s13293-022-00426-2 · Cited by 12
Ramachandran, S. S., Osterhaus, S. R., Karp, P. H., Welsh, M. J., & McCray, P. B. (2014) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2014-0007oc · Cited by 12
Deletion of CD44 promotes adipogenesis by regulating PPARγ and cell cycle-related pathways
Weng, X., Jiang, H., Walker, D. J., Zhou, H., Lin, D., Wang, J., & Kang, L. (2024) · Journal of Endocrinology · doi:10.1530/joe-24-0079 · Cited by 11
Kashirina, D. N., Pastushkova, L. K., Custaud, M., Доброхотов, И. В., Brzhozovsky, A. G., Navasiolava, N. M., Nosovsky, A. M., Kononikhin, A. S., Николаев, Е. Н., & Larina, I. (2017) · Acta Astronautica · doi:10.1016/j.actaastro.2017.04.020 · Cited by 11
Martínez-Montes, Á. M., Muiños-Bühl, A., Fernández, A., Folch, J. M., Ibáñez‐Escriche, N., & Fernández, A. I. (2016) · Mammalian Genome · doi:10.1007/s00335-016-9674-3 · Cited by 13
Hansel, C., Erschfeld, S., Baues, M., Lammers, T., Weiskirchen, R., Trautwein, C., Kroy, D. C., & Drescher, H. K. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00244 · Cited by 10
The computational Patient has diabetes and a COVIDPreprint
Barbiero, P., & Lió, P. (2020) · medRxiv · doi:10.1101/2020.06.10.20127183 · Cited by 14
Wu, Y., Ma, J., Chen, J., Liu, X., Wang, Z., Luo, L., & Sun, C. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.10.005 · Cited by 10
Li, G., Fang, X., Liu, Y., Lu, X., Liu, Y., Li, Y., Zhao, Z., Liu, B., & Yang, R. (2024) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c02434 · Cited by 8
Hidden pathway: the role of extracellular matrix in type 2 diabetes mellitus–related sarcopenia
Sun, Y., Zhang, Z., Wang, Y., Wu, X., Sun, Y., Lou, H., Xu, J., Yao, J., & Cong, D. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1560396 · Cited by 9
Unveiling the dynamics of acetylation and phosphorylation in SGBS and 3T3-L1 adipogenesis
Aldehoff, A. S., Karkossa, I., Goerdeler, C., Krieg, L., Schor, J., Engelmann, B., Wabitsch, M., Landgraf, K., Hackermüller, J., Körner, A., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109711 · Cited by 8
Wang, Z., Liao, T., Zhou, Z., Wang, Y., Diao, Y., Strappe, P., Prenzler, P. D., Ayton, J., & BLANCHARD, C. L. (2016) · Toxicology Letters · doi:10.1016/j.toxlet.2016.06.2101 · Cited by 8
Treatment of Hyaluronan Accumulation Ameliorates High-Fat Diet–Induced Insulin Resistance in Mice
Fogelstrand, P., & Borén, J. (2013) · Diabetes · doi:10.2337/db13-0261 · Cited by 8
Hymecromone Promotes Longevity and Insulin Sensitivity in Mice
Nagy, N., Czepiel, K. S., Kaber, G., Stefanovski, D., Hargil, A., Pennetzdorfer, N., Targ, R. W., Reghupaty, S. C., Wight, T. N., Vernon, R. B., et al. (2024) · Cells · doi:10.3390/cells13201727 · Cited by 7
Chen, I.-B. D., Rathi, V. K., DeAndrade, D. S., & Jay, P. Y. (2012) · Physiological Genomics · doi:10.1152/physiolgenomics.00116.2012 · Cited by 7
Gene expression profiles in whole blood and associations with metabolic dysregulation in obesity
Cox, A. J., Zhang, P., Evans, T. J., Scott, R. J., Cripps, A. W., & West, N. P. (2017) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2017.07.001 · Cited by 7
Zhou, D., Mou, X., Liu, K., Liu, W., Xu, Y., & Zhou, D. (2020) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2020.9188 · Cited by 6
Sun, L. Z., Li, L., Sun, T., Zhang, L., Li, C., Xu, M., Hou, Y., Peng, J., Hou, M., & Wang, L. (2021) · International Immunopharmacology · doi:10.1016/j.intimp.2021.107502 · Cited by 5
Arumuganainar, D., Naik, V. K., Ramadoss, R., Balasundaram, A., & Victor, D. J. (2024) · Dental and Medical Problems · doi:10.17219/dmp/156225 · Cited by 5
Qin, F., Yan, Y., Yang, N., & Hao, Y. (2024) · Experimental and Clinical Endocrinology & Diabetes · doi:10.1055/a-2298-4593 · Cited by 5
Xian, J., Huang, Y., Bai, J., Liao, Q., Chen, Q., Cheng, W., Su, Z., Li, S., Wu, Y., Li, J., et al. (2024) · Phytotherapy Research · doi:10.1002/ptr.8400 · Cited by 5
Shibuta, T., Takada, Y., Nishinosono, S., Yasuda, S., Ono, Y., Hirooka, Y., Irikura, D., Saito, K., & Umemura, T. (2025) · Extracellular Vesicles and Circulating Nucleic Acids · doi:10.20517/evcna.2024.82 · Cited by 4
Ahmmed, R., Antu, U. S., Noor, T., Faysal, M. F., Sarker, A., Mahmud, S., Ajadee, A., & Mollah, M. N. H. (2026) · Journal of Genetic Engineering and Biotechnology · doi:10.1016/j.jgeb.2026.100667 · Cited by 4
Cao, X., Gao, T., Lv, F., Wang, Y., Li, B., & Wang, X. (2024) · Nanotechnology · doi:10.1088/1361-6528/ad7034 · Cited by 4
Stamper, B. D. (2014) · Drug Metabolism Reviews · doi:10.3109/03602532.2014.978081 · Cited by 4
Mencucci, M. V., Flores, L. E., Gagliardino, J. J., Abba, M. C., & Maiztegui, B. (2020) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3359 · Cited by 4
Wang, Z., Zhang, Y., Shi, R., Zhou, Z., Wang, F., & Strappe, P. (2015) · SpringerPlus · doi:10.1186/s40064-015-0873-2 · Cited by 4
Grigoryev, D. N., Dalal, J. D., Becker, M. L., & Ye, S. Q. (2014) · BMC Hematology · doi:10.1186/2052-1839-14-7 · Cited by 4
Roham, P. H., Yadav, S. S., Senthilnathan, B., Potdar, P., Roy, S., & Sharma, S. (2025) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0216 · Cited by 3
Cefalu, W. T., Andersen, D. K., Arreaza-Rubín, G., Pin, C. L., Sato, S. M., Verchere, C. B., Woo, M., Rosenblum, N. D., Rosenblum, N. D., Cefalu, W. T., et al. (2021) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2021.09.126 · Cited by 3
Self-citationAnalysis of key genes and pathways associated with the pathogenesis of Type 2 diabetes mellitusPreprint
Alur, V. C., Raju, V., Vastrad, B. M., Vastrad, C. M., & Kotrashetti, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.12.456106 · Cited by 3
Differences in splicing factors may predict type 2 diabetes remission in the CORDIOPREV study
Ojeda‐Rodríguez, A., Torres-Peña, J. D., Larriva, A. P. A.-D., Rangel-Zúñiga, O. A., Podadera-Herreros, A., Boughanem, H., G-García, M. E., López-Moreno, A., Katsiki, N., Luque, R. M., et al. (2024) · iScience · doi:10.1016/j.isci.2024.111527 · Cited by 2
Emine, K., Olgun, N., & Cigdem, I. (2014) · TANG [HUMANITAS MEDICINE] · doi:10.5667/tang.2014.0016 · Cited by 2
Bai, S., Chaurasiya, A. H., Banarjee, R., Walke, P. B., Rashid, F., Unnikrishnan, A., & Kulkarni, M. J. (2020) · ACS Omega · doi:10.1021/acsomega.0c01318 · Cited by 2
Predicting Gene Regulatory Interactions Using Natural Genetic Variation
John, M., Grimm, D. G., & Korte, A. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3354-0_18 · Cited by 2
Gao, M., Wang, H., Li, N., Qiao, Y., Liu, H., Li, W., Wang, S., Li, J., Yu, Z., Hu, G., et al. (2024) · Journal of Proteomics · doi:10.1016/j.jprot.2024.105268 · Cited by 2
Stamper, B. D. (2013) · Transcriptomics Open Access · doi:10.4172/2329-8936.1000e104 · Cited by 2
Sirota, M., & Chen, B. (2015) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/2788504 · Cited by 2
Self-citationCopy Number Networks to Guide Combinatorial Therapy of Cancer and Proliferative Disorders
Lin, A., & Smith, D. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-802508-6.00021-1 · Cited by 2
Jia, X., Toda, K., He, L., Miao, D., Yamada, S., Yu, L., & Kodama, K. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac256 · Cited by 2
Self-citationGao, L., Huang, C., Li, H., Wu, S., Zhou, X., & Ying, C. (2023) · Epigenomics · doi:10.2217/epi-2023-0149 · Cited by 2
Copy number networks to guide combinatorial therapy for cancer and other disordersPreprint
Lin, A., & Smith, D. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/005942 · Cited by 2
CD44 Controls Endothelial Proliferation and Functions as Endogenous Inhibitor of AngiogenesisPreprint
Pink, A., Školnaja, M., Päll, T., & Valkna, A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/049494 · Cited by 2
Vastrad, B. M., & Vastrad, C. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.24.563759 · Cited by 2
Application of hyaluronic acid: Therapeutics, pharmaceuticals, and cosmetics
Bisht, P., Kumbhar, A. P., Ranjan, O. P., Sinha, M. K., Kovela, R. K., Vaishali, K., & Kumar, N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14042-6.00006-3 · Cited by 1
Kotturshetti, I., Vastrad, B. M., Kori, V., Vastrad, C. M., & Kotrashetti, S. (2024) · Italian Journal of Medicine · doi:10.4081/itjm.2024.1830 · Cited by 3
Pharmacogenomics of Cardiovascular Complications in Diabetes and Obesity
Chapalamadugu, K. C., Panguluri, S. K., Miranda, A. C., Sneed, K., & Tipparaju, S. M. (2014) · Recent Patents on Biotechnology · doi:10.2174/1872208309666140904123023 · Cited by 1
Plant Gene Regulatory Networks
Kaufmann, K., & Vandepoele, K. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3354-0
Vastrad, B. M., & Vastrad, C. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.16.537071 · Cited by 1
La médecine génomique, une réalité en pleine évolution. Première partie
Lamoril, J., & Bogard, M. (2013) · Immuno-analyse & Biologie Spécialisée · doi:10.1016/j.immbio.2012.12.002 · Cited by 1
CD44 immune receptor linked to diabetes
Harrison, C. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3765
Pinnick, K. E., & Karpe, F. (2014) · Frontiers in diabetes · doi:10.1159/000362472
Bioinformatics approach for data management about bone cells grown on substitute materials
Viti, F., Ivan Merelli, A., Scaglione, S., & Milanesi, L. (2012) · F1000Research · doi:10.7490/f1000research.1092797.1
Widescale analysis of transcriptomics data using cloud computing methods
Owen, A. M. (2016) · Open Access at Essex (University of Essex)
Functional genomics of the VNN1 gene
Diepeveen, L. A. (2013) · UWA Profiles and Research Repository (UWA)
Principled "convergence" non-coding rare variant association testing in complex disease
Sosa, D. N. (2017) · DSpace@MIT (Massachusetts Institute of Technology)
The Computational Patient has Diabetes and a COVIDPreprint
Barbiero, P., & Lió, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06435
Foundations and applications of computational genomics
Kanu, G. A., Mouselly, A., & Mohamed, A. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27574-6.00007-2
Identification and Validation of Genes Involved in Energy Metabolism in Polycystic Ovary Syndrome.
Zhang, J., Jie, H., Mai, Q., Chen, L., & Zhou, C. (2025) · Europe PMC (PubMed Central) · doi:10.30498/ijb.2025.514574.4102
VerHague, M., Albright, J., Barron, K. A., Kim, M., & Bennett, B. J. (2022) · Figshare · doi:10.6084/m9.figshare.19582401
Sarkar, K., Bank, S., Chatterjee, A., Dutta, K., Das, A., Chakraborty, S., Paul, N., Sarkar, J., De, S., Ghosh, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26607973
The roles of endoplasmic reticulum stress and Bax inhibitor-1 in non-alcoholic fatty liver disease
Lebeaupin, C. (2018) · theses.fr (ABES) · doi:10.70675/de500ab2z3c59z484dz8f58z7c3173626c42
Conception & études de biodistribution de liposomes ciblant CD44 dans un modèle de cancer du sein
Antoni, F. (2019) · theses.fr (ABES) · doi:10.70675/49b8fab4zfbccz4514z8a3dzd079b2730e99
Canivet, C. M. (2019) · theses.fr (ABES) · doi:10.70675/d03174c8z45cez439ezba77z41b6ed9403b3
Wietecha, M. S., Pang, J., Kang, M., Hafedi, A., Walsdorf, S., Keiser, S., Maienschein‐Cline, M., & Koh, T. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.26.720829
Ali, S. A., Qiang, P., Zhou, X., Yang, X., Li, S., Shen, Y., Chen, Z., & Zhang, L. F. (2026) · Biogerontology · doi:10.1007/s10522-026-10476-3
Metadata
- Authors
- Kodama, K., Horikoshi, M., Toda, K., Yamada, S., Hara, K., Irie, J., Sirota, M., Morgan, A. A., Chen, R., Ohtsu, H., Maeda, S., Kadowaki, T., & Butte, A. J.
- Affiliations
- Lucile Packard Children's Hospital (US)
- The University of Tokyo (JP)
- Kitasato Institute Hospital (JP)
- Keio University (JP)
- RIKEN (JP)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-04-12
- Volume
- 109
- Issue
- 18
- Pages
- 7049-7054
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3344989
- OpenAlex
- W2044994793
- Cited by
- 171
- References
- 43
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 16 · 2023: 18 · 2022: 9 · 2021: 10 · 2020: 14 · 2019: 8 · 2018: 17 · 2017: 10 · 2016: 10 · 2015: 20 · 2014: 11 · 2013: 14 · 2012: 7
- FWCI
- 12.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- Lunds Universitet
- Kitasato University
- School of Medicine, Stanford University
- Japan Society for the Promotion of Science (20229008)
- U.S. National Library of Medicine (K22LM008261, R01LM009719, T15LM007033)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adipokines, Inflammation, and Metabolic Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Physiology (medical) › Adipose Tissue and Metabolism
- Keywords
- type 2 diabetes, CD44, insulin resistance, adipose tissue inflammation, adipose tissue macrophages, obesity, diabetic mouse model, anti-CD44 antibody, glycemic control
- MeSH terms
- Adipose Tissue, Aged, Animals, Blood Glucose, Diabetes Mellitus, Type 2, Dietary Fats, Female, Humans, Insulin Resistance, Macrophages, Male, Mice, Inbred C57BL, Middle Aged, Obesity, Panniculitis, Gene Expression, Case-Control Studies, Mice, Knockout, Hyaluronan Receptors, Oligonucleotide Array Sequence Analysis, Mice, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
