The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index; 1 of the 6 sources its claims cite were checked for support.
Homeostasis model assessment: insulin resistance and ?-cell function from fasting plasma glucose and insulin concentrations in man
Open accessAbstract
The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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Claims the paper makes
1 of 6 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The fasting plasma insulin concentration is largely determined by the glucose concentration [3]”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
What the sources say
- Partly supportedcites [3] McCarthy ST, Harris F, Turner RC (1977) Glucose control of basal insulin secretion in diabetes. Diabetologia 13: 93?97 sourceExperimental — accuracy not yet measured
The Results and Discussion (page unavailable in our copy) show that changing plasma glucose affects basal insulin secretion. But they also report markedly impaired responses in diabetic subjects, including cases where insulin remained nearly constant despite a substantial glucose decrease. The paper therefore supports glucose as an influence, but not the unqualified claim that it largely determines fasting insulin concentration. (LLM, conf=0.84)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimate of deficient/3cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (R~ = 0.61, p< 0.01)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (R~=0.88,p<0.0001)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was no correlation with any aspect of insulin-receptor binding.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the fasting insulin concentration (P~ = 0.81, p < 0.0001)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean coefficient of variation over the two studies was 32%.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Reproducibility of estimate of /3-cell function by HOMA
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“in both groups together (1% 0.61, p < 0.01 : Fig. 3).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Comparisons of methods of assessing ~3-cell function
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and with the estimate from the intravenous glucose tolerance test (R~ = 0.64, p < 0.05).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and in both groups together: 1% = 0.64, p< 0.05; Fig. 3).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with intravenous glucose tolerance test.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Neither receptor numbers or any aspect of high or low affinity binding correlated with other measures of insulin resistance (HOMA, clamp, basal insulin or CIGMA: ~ all < 0.55; NS).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with the hyperglycaemic clamp. The
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
- It may state two separate claims as one. 2
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for/3-cell deficit) limits its use”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean coefficient of variation over the two studies was 31%.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Hyperglycaemic clamp : ~-cell function (%)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The degree of the hyperglycaemia does not correlate significantly with either the degrees of abnormal fl-cell function or insulin resistance.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
The authors state this as an assumption.
Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
“Assuming that normal-weight normal subjects aged < 35 years have 100% r-cell function, and an insulin resistance of 1, the values for a patient can be assessed from the insulin and glucose concentrations by the formulae: r-cell”
The paper relies on this without support.
The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
“The plasma glucose:insulin response for normal subjects, equivalent to the C-peptide response, is insulin = 5 (glucose-3.5).”
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?
The cited source says what the claim attributes to it.
In the citing paper
“After the unprocessed diet, fasting glucose and insulin levels tended to decrease compared to baseline and the homeostasis model assessment of insulin resistance (HOMA-IR) (Matthews et al., 1985) was significantly decreased compared to baseline.”
From Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake (2019) · cites this paper as Matthews et al., 1985
The cited source says what the claim attributes to it.
In the citing paper
“The Homeostasis Model Assessment of Insulin Resistance (HoMA-IR) was calculated according to Matthews et al. [25].”
From Chronotype: A Tool to Screen Eating Habits in Polycystic Ovary Syndrome? (2022) · cites this paper as [25]
The cited source says what the claim attributes to it.
In the citing paper
“AAV9-mTERT treated mice also showed a trend for an improved glucose uptake and a better homeostatic model assessment (HOMA (Heikkinen et al, 2007; Matthews et al, 1985)) compared to the AAV9-eGFP controls, although differences did not reach statistical significance (Supporting Information Fig S4D and E).”
From Telomerase gene therapy in adult and old mice delays aging and increases longevity without increasing cancer (2012) · cites this paper as Matthews et al, 1985
In the citing paper
“Surrogate markers of peripheral insulin activity, such as homeostasis model assessment and whole-body insulin sensitivity indexes, could be useful as well in the early identification of patients with IR, before overt DM sets in ().”
From Glucose Metabolism Abnormalities in Cushing Syndrome: From Molecular Basis to Clinical Management (2017) · cites this paper as [240]
In the citing paper
“ndirect estimates of insulin resistance, such as HOMA-IR 63 and QUICKI, 64 are derived from fasting S-insulin and glucose concentrations and have been widely used in both nondiabetic and type 2 diabetic subjects as surrogates of insulin sensitivity.65,66”
From Increased liver fat, impaired insulin clearance, and hepatic and adipose tissue insulin resistance in type 2 diabetes. (2008) · cites this paper as [63]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Normal and diabetic group sizes are reversed for the CIGMA comparison
MinorThe methods say the CIGMA comparison used 12 normal subjects and 11 diabetic patients, but the results report 11 normal subjects and 12 diabetic subjects.
“The same 12 normal subjects and 11 Type 2 diabetic patients came to the hospital fasting on a separate day for a continuous infusion”
“HOMA compared with CIGMA.The HOMA estimate of insulin resistance from the mean of 15 stressed fasting samples before the CIGMA correlated with that measured by CIGMA in 11 normal subjects (R~ = 0.69, p < 0.02), in 12 diabetic subjects (R~ 0.97, p < 0.0001)”
Citations
Cites
Glucose clamp technique: a method for quantifying insulin secretion and resistance.
DeFronzo, R. A., Tobin, J. D., & Andres, R. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.237.3.e214 · Cited by 7,425
Quantitative estimation of insulin sensitivity.
Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.236.6.e667 · Cited by 1,874 · cited 1×
“This use of modelling to provide a reference graph contrasts with other models where an individual patient's data have to be entered into a computer to determine the relationships between rapidly changing variables, such as occur after an intravenous glucose test [19].”
Method[WHO Expert Committee on diabetes mellitus].
Reitsma, W. D., & Terpstra, J. (1981) · PubMed · Cited by 556
Turner, R. C., Holman, R. R., Matthews, D., Hockaday, T. D. R., & Peto, J. (1979) · Metabolism · doi:10.1016/0026-0495(79)90146-x · Cited by 388
Self-citationCharacteristics of Human Erythrocyte Insulin Receptors
Gambhir, K. K., Archer, J. A., & BRADLEY, C. J. (1978) · Diabetes · doi:10.2337/diab.27.7.701 · Cited by 290
The Insulin Receptor: Its Role in Insulin Resistance of Obesity and Diabetes
Olefsky, J. M. (1976) · Diabetes · doi:10.2337/diab.25.12.1154 · Cited by 289
Hosker, J. P., Matthews, D. R., Rudenski, A. S., Burnett, M. A., Darling, P., Bown, E. G., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280882 · Cited by 287
Self-citationPulsatile Insulin Has Greater Hypoglycemic Effect Than Continuous Delivery
Matthews, D. R., Naylor, B. A., Jones, R., Ward, G. M., & Turner, R. C. (1983) · Diabetes · doi:10.2337/diab.32.7.617 · Cited by 256
Self-citationControl of pulsatile insulin secretion in man
Matthews, D. R., Lang, D. A., Burnett, M. A., & Turner, R. C. (1983) · Diabetologia · doi:10.1007/bf00282705 · Cited by 175
Self-citationTHE BASAL PLASMA GLUCOSE: A SIMPLE RELEVANT INDEX OF MATURITY‐ONSET DIABETES
HOLMAN, R. R., & TURNER, R. C. (1981) · Clinical Endocrinology · doi:10.1111/j.1365-2265.1981.tb00196.x · Cited by 64
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Metropolitan Life Assurance Co (1959) Net weight standard for men and women. Stat Bull Metrol Life Found 40: 1?4
- Matthews DR, Hosker JP (1984) An unbiased, flexible computer programme for glucose clamping, with graphics and running statistics. Diabetologia 27: 308?309 A
Cited by
Sorted by citations
Items 2,801–2,900 of 31,469
Koopman, R., MANDERS, R. J. F., Zorenc, A. H. G., Hul, G. B. J., Kuipers, H., KEIZER, H. A., & van Loon, L. J. C. (2005) · European Journal of Applied Physiology · doi:10.1007/s00421-004-1307-y · Cited by 110
Effect of medical and surgical weight loss on endothelial vasomotor function in obese patients
Gokce, N., Vita, J. A., McDonnell, M. E., Forse, R. A., Istfan, N. W., Mattera, M. S., Lipinska, I., Keaney, J. F., & Apovian, C. M. (2005) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2004.09.016 · Cited by 109
Mechanisms for abnormal postprandial glucose metabolism in type 2 diabetes
Woerle, H. J., Szoke, E., Meyer, C., Dostou, J. M., Wittlin, S. D., Gosmanov, N. R., Welle, S. L., & Gerich, J. E. (2005) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00529.2004 · Cited by 109
Nagaretani, H., Nakamura, T., Funahashi, T., Kotani, K., Miyanaga, M., Tokunaga, K., Takahashi, M., Nishizawa, H., Kishida, K., Kuriyama, H., et al. (2001) · Diabetes Care · doi:10.2337/diacare.24.12.2127 · Cited by 108
Hivert, M., Manning, A. K., McAteer, J. B., Dupuis, J., Fox, C. S., Cupples, L. A., Meigs, J. B., & Florez, J. C. (2008) · Diabetes · doi:10.2337/db08-1339 · Cited by 108
Adiponectin, Adiposity, and Insulin Resistance in Children and Adolescents
Punthakee, Z., DELVIN, E. E., O’Loughlin, J. L., Paradis, G., Levy, E. H., Platt, R. W., & Lambert, M. A. (2006) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2005-2346 · Cited by 109
Zaqout, M., Michels, N., Bammann, K., Ahrens, W., Sprengeler, O., Molnár, D., Hadjigeorgiou, C., Eiben, G., Konstabel, K., Russo, P., et al. (2016) · International Journal of Obesity · doi:10.1038/ijo.2016.22 · Cited by 109
High prevalence of metabolic syndrome in Hong Kong Chinese—comparison of three diagnostic criteria
Ko, G. T.-C., Cockram, C. S., Chow, C., Yeung, M. W., Chan, W. B., So, W., Chan, N. N., & Chan, J. C. N. (2005) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2004.11.015 · Cited by 108
Puurunen, J., Piltonen, T., Morin-Papunen, L., Perheentupa, A., Järvelä, I., Ruokonen, A., & Tapanainen, J. S. (2011) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-0039 · Cited by 108
Favourable metabolic effects of a eucaloric lower‐carbohydrate diet in women with PCOS
Gower, B. A., Chandler‐Laney, P. C., Ovalle, F., Goree, L. L., Azziz, R., Desmond, R. A., Granger, W. M., Goss, A. M., & Bates, G. W. (2013) · Clinical Endocrinology · doi:10.1111/cen.12175 · Cited by 108
Škrha, J., Haas, T., Šindelka, G., Prázný, M., Widimský, J., Cibula, D., & Svačina, Š. (2004) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2002-030024 · Cited by 108
Vadini, F., Simeone, P. G., Boccatonda, A., Guagnano, M. T., Liani, R., Tripaldi, R., Di Castelnuovo, A., Cipollone, F., Consoli, A., & Santilli, F. (2020) · International Journal of Obesity · doi:10.1038/s41366-020-0535-5 · Cited by 110
“Right after a frequent sampling OGTT was performed for assessment of insulin sensitivity (Matsuda index, HOMA-IR) [20, 21], and beta cell function (OGTT beta index) [22].”
MethodPakdeechote, P., Bunbupha, S., Kukongviriyapan, U., Prachaney, P., Khrisanapant, W., & Kukongviriyapan, V. (2014) · Nutrients · doi:10.3390/nu6010355 · Cited by 109
Rider, O. J., Tayal, U., Francis, J. M., Ali, M. K., Robinson, M. R., Byrne, J. P., Clarke, K., & Neubauer, S. (2010) · Obesity · doi:10.1038/oby.2010.64 · Cited by 108
Sengupta, S. M., Parrilla-Escobar, M. A., Klink, R., Fathalli, F., Ng, Y. K., Stip, É., Baptista, T., MALLA, A. K., & Joober, R. (2008) · Schizophrenia Research · doi:10.1016/j.schres.2008.02.013 · Cited by 108
Guerrero‐Romero, F., Simental‐Mendía, L. E., Hernández-Ronquillo, G., & Rodríguez‐Morán, M. (2015) · Diabetes & Metabolism · doi:10.1016/j.diabet.2015.03.010 · Cited by 109
Petta, S., Amato, M. C., Cabibi, D., Cammà, C., Marco, V. D., Giordano, C. S. D. S., Galluzzo, A., & Craxı̀, A. (2010) · Hepatology · doi:10.1002/hep.23859 · Cited by 108
Adipose Tissue Inflammation and Adiponectin Resistance in Patients With Advanced Heart Failure
Khan, R. S., Kato, T., Chokshi, A. D., Chew, M., Yu, S., Wu, C. C. N., Singh, P., Cheema, F. H., Takayama, H., Harris, C. R., et al. (2012) · Circulation Heart Failure · doi:10.1161/circheartfailure.111.964031 · Cited by 109
Yılmaz, M. İ., Sağlam, M., Qureshi, A. R., Carrero, J. J., Çağlar, K., Eyileten, T., Sönmez, A., Cakir, E., Oğuz, Y., Vural, A., et al. (2008) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfm828 · Cited by 109
Phillips, C. M., Tierney, A. C., Pérez‐Martínez, P., Defoort, C., Blaak, E. E., Gjelstad, I. M. F., López‐Miranda, J., Kieć-Klimczak, M., Malczewska-Malec, M., Drevon, C. A., et al. (2013) · Obesity · doi:10.1002/oby.20263 · Cited by 110
Vitamin D, parathyroid hormone and the metabolic syndrome in middle-aged and older European men
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The correlation between pancreatic steatosis and metabolic syndrome in a Chinese population
Zhou, J., Li, M., Zhang, D., Lin, H., Dai, X., Sun, X., Li, J., Song, L., Peng, H., & Wen, M.-M. (2016) · Pancreatology · doi:10.1016/j.pan.2016.03.008 · Cited by 110
Geraghty, A., Alberdi, G., O'Sullivan, E., O'Brien, E., Crosbie, B., Twomey, P., & Mcauliffe, F. (2016) · PLoS ONE · doi:10.1371/journal.pone.0161206 · Cited by 95
Lean mass, grip strength and risk of type 2 diabetes: a bi-directional Mendelian randomisation study
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Han, E., Lee, Y., Kim, Y. D., Kim, B. K., Park, J. Y., Kim, D. Y., Ahn, S. H., Lee, B., Kang, E. S., Soo, B., et al. (2020) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000000572 · Cited by 109
Chew, C., Reynolds, J. A., Lertratanakul, A., Wu, P. W., Urowitz, M. B., Gladman, D. D., Fortin, P. R., Bae, S., Gordon, C., Clarke, A. E., et al. (2021) · Lara D. Veeken · doi:10.1093/rheumatology/keab090 · Cited by 108
Yan, F., Yan, S., Wang, J., Cui, Y., Chen, F., Fang, F., & Cui, W. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01664-9 · Cited by 115
Li, H., Bao, Y., Xu, A., Pan, X., Lu, J., Wu, H., Lu, H., Xiang, K., & Jia, W. (2009) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2008-2331 · Cited by 108
Ng, M. C. Y., Tam, C. H. T., Lam, V. K. L., So, W., Ronald, C. W., & Chan, J. C. N. (2007) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2007-0849 · Cited by 108
Cortisol and the metabolic syndrome in South Asians
Ward, A. M. V., Fall, C. H. D., Stein, C., Kumaran, K., Veena, S. R., Wood, P. J., Syddall, H. E., & Phillips, D. I. W. (2003) · Clinical Endocrinology · doi:10.1046/j.1365-2265.2003.01750.x · Cited by 108
A 5‐year follow‐up study of disease incidence in men with an abnormal hormone pattern
Rosmond, R., Wallerius, S., Wanger, P., Martin, L., Holm, G., & Björntorp, P. A. (2003) · Journal of Internal Medicine · doi:10.1046/j.1365-2796.2003.01205.x · Cited by 108
MAFFEI, L. E., Rochira, V., Zirilli, L., Paula, A., Aranda, C., Fabre, B. R., Simone, M. L., Pignatti, E., Simpson, E. R., Houssami, S., et al. (2007) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2007.02864.x · Cited by 109
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Loria, P., Lonardo, A., Lombardini, S., Carulli, L., Verrone, A. M., Ganazzi, D., Rudilosso, A., D’Amico, R., Bertolotti, M., & Carulli, N. (2005) · Journal of Gastroenterology and Hepatology · doi:10.1111/j.1440-1746.2005.03924.x · Cited by 106
Höybye, C. (2003) · Growth Hormone & IGF Research · doi:10.1016/j.ghir.2003.09.003 · Cited by 109
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Ng, T. W., Watts, G. F., Farvid, M. S., Chan, D. C., & Barrett, P. H. R. (2005) · Diabetes · doi:10.2337/diabetes.54.3.795 · Cited by 110
Role of dietary beta-glucans in the prevention of the metabolic syndrome
Cloetens, L., Ulmius, M., Johansson‐Persson, A., Åkesson, B., & Önning, G. (2012) · Nutrition Reviews · doi:10.1111/j.1753-4887.2012.00494.x · Cited by 112
Kitabchi, A. E., McDaniel, K. A., Wan, J. Y., Tylavsky, F. A., Jacovino, C., Sands, C., Nyenwe, E. A., & Stentz, F. B. (2013) · Diabetes Care · doi:10.2337/dc12-1912 · Cited by 111
Short-term appetite control in response to a 6-week exercise programme in sedentary volunteers
Martins, C., Truby, H., & Morgan, L. (2007) · British Journal Of Nutrition · doi:10.1017/s000711450774922x · Cited by 108
Novel Metabolic Risk Factors for Heart Failure
Ingelsson, E., Ärnlöv, J., Sundström, J., Zethelius, B., Vessby, B. O., & Lind, L. (2005) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2005.07.059 · Cited by 108
Robins, S., Lyass, A., Zachariah, J., Massaro, J., & Vasan, R. (2011) · Arteriosclerosis, Thrombosis and Vascular Biology · doi:10.1161/atvbaha.110.219055 · Cited by 111
Bowen, J., Noakes, M., & Clifton, P. M. (2007) · International Journal of Obesity · doi:10.1038/sj.ijo.0803665 · Cited by 108
Magkos, F., Tsekouras, Y., Kavouras, S. A., Mittendorfer, B., & Sidossis, L. S. (2007) · Clinical Science · doi:10.1042/cs20070134 · Cited by 112
Low-salt diet increases insulin resistance in healthy subjects
Garg, R., Williams, G. H., Hurwitz, S., Brown, N. J., Hopkins, P. N., & Adler, G. K. (2010) · Metabolism · doi:10.1016/j.metabol.2010.09.005 · Cited by 108
Safety profile of conjugated linoleic acid in a 12-month trial in obese humans
Whigham, L. D., O’Shea, M., Mohede, I., et al. (2004) · Food and Chemical Toxicology · doi:10.1016/j.fct.2004.06.008 · Cited by 108
Valproate therapy and nonalcoholic fatty liver disease
Luef, G. J., Waldmann, M., Sturm, W., Naser, A., Trinka, E., Unterberger, I., Bauer, G. J., & Lechleitner, M. (2004) · Annals of Neurology · doi:10.1002/ana.20074 · Cited by 107
Cheung, L. P., Ma, R. C. W., Lam, P. M., Lok, I. H., Haines, C. J., So, W. Y., Tong, P. C. Y., Cockram, C. S., Chow, C. C., & Goggins, W. B. (2008) · Human Reproduction · doi:10.1093/humrep/den090 · Cited by 108
Insulin Resistance, Inflammatory Biomarkers, and Adipokines in Patients with Chronic Kidney Disease
de Vinuesa, S. G., Goicoechea, M., Kanter, J., Puerta, M., Cachofeiro, V., Lahera, V., Gómez-Campderá, F. J., & Luño, J. (2006) · Journal of the American Society of Nephrology · doi:10.1681/asn.2006080916 · Cited by 108
Evaluation of Endothelial Function in Subclinical Hypothyroidism and Subclinical Hyperthyroidism
Çıkım, A. S., Oflaz, H., Özbey, N., Çıkım, K., Umman, S., Meriç, M., Sencer, E., & Molvalilar, S. (2004) · Thyroid · doi:10.1089/1050725041692891 · Cited by 108
Low Adiponectin Levels Predict Type 2 Diabetes in Mexican Children
Cruz, M., García-Macedo, R., García-Valerio, Y., Gutiérrez, M., Medina-Navarro, R., Durán-Reyes, G., Wacher, N. H., & Kumate, J. (2004) · Diabetes Care · doi:10.2337/diacare.27.6.1451 · Cited by 108
Berberoğlu, Z., Gürsoy, A., Bayraktar, N., Yazıcı, A. C., Tütüncü, N. B., & Demirağ, N. G. (2007) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2007-0431 · Cited by 106
Role of Insulin Resistance in Predicting Progression to Type 1 Diabetes
Xu, P., Cuthbertson, D. D., Greenbaum, C. J., Palmer, J. P., Krischer, J. P., & for the Diabetes Prevention Trial–Type 1 Study Group (2007) · Diabetes Care · doi:10.2337/dc06-2389 · Cited by 108
Intensive Integrated Therapy of Type 2 Diabetes
Gæde, P., & Pedersen, O. (2004) · Diabetes · doi:10.2337/diabetes.53.suppl_3.s39 · Cited by 108
Benbassat, C. A., Maki, K. C., & Unterman, T. G. (1997) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.82.5.3930 · Cited by 116
Tumour necrosis factor-alpha gene promoter polymorphism and decreased insulin resistance
Day, C. P., Grove, J., Daly, A. K., Stewart, M., Avery, P. J., & Walker, M. (1998) · Diabetologia · doi:10.1007/s001250050926 · Cited by 108
Petersson, H., Basu, S., Cederholm, T., & Risérus, U. (2008) · British Journal Of Nutrition · doi:10.1017/s0007114507871674 · Cited by 107
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Eslam, M., Aparcero, R., Kawaguchi, T., Campo, J. A. D., Sata, M., Khattab, M. A., & Romero‐Gómez, M. (2011) · Alimentary Pharmacology & Therapeutics · doi:10.1111/j.1365-2036.2011.04716.x · Cited by 107
Catechin and quercetin attenuate adipose inflammation in fructose‐fed rats and 3T3‐L1 adipocytes
Prieto, M. A. V., Bettaieb, A., Lanzi, C. R., Soto, V. C., Perdicaro, D. J., Galmarini, C. R., Haj, F. G., Miatello, R. M., & Oteiza, P. I. (2015) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201400631 · Cited by 111
Quantitation of serum angiopoietin-like proteins 3 and 4 in a Finnish population sample
Robciuc, M. R., Tahvanainen, E., Jauhiainen, M., & Ehnholm, C. (2009) · Journal of Lipid Research · doi:10.1194/jlr.m002618 · Cited by 107
Ciresi, A., Amato, M. C., Guarnotta, V., Castro, F. L., & Giordano, C. S. D. S. (2013) · Clinical Endocrinology · doi:10.1111/cen.12204 · Cited by 107
Role of Cytokines in the Pathogenesis of Non-Alcoholic Fatty Liver Disease
Das, S. K., & Balakrishnan, V. (2011) · Indian Journal of Clinical Biochemistry · doi:10.1007/s12291-011-0121-7 · Cited by 114
Lifecourse Childhood Adiposity Trajectories Associated With Adolescent Insulin Resistance
Huang, R., de Klerk, N. H., Smith, A. J., Kendall, G. E., Landau, L. I., Mori, T. A., Newnham, J. P., Stanley, F. J., Oddy, W. H., Hands, B. P., et al. (2011) · Diabetes Care · doi:10.2337/dc10-1809 · Cited by 107
Bhuvaneswari, S., Elumalai, A., Viswanathan, P., & Anuradha, C. V. (2010) · Process Biochemistry · doi:10.1016/j.procbio.2010.05.016 · Cited by 107
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Villarini, A., Pasanisi, P., Traina, A., Mano, M. P., Bonanni, B., Panico, S., Scipioni, C., Galasso, R., Paduos, A., Simeoni, M., et al. (2012) · Tumori Journal · doi:10.1177/030089161209800101 · Cited by 105
Self-adjusting synthetic gene circuit for correcting insulin resistance
Ye, H., Xie, M., Xue, S., Hamri, G. C., Yin, J., Zulewski, H., & Fussenegger, M. (2016) · Nature Biomedical Engineering · doi:10.1038/s41551-016-0005 · Cited by 108
Demmer, R. T., Breskin, A., Rosenbaum, M. S., Zuk, A. M., LeDuc, C. A., Leibel, R. L., Paster, B. J., Desvarieux, M., Jacobs, D. R., & Papapanou, P. N. (2016) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.12664 · Cited by 107
Phosat, C., Phienluphon, P. P., Chumpathat, N., Prangthip, P., Chantratita, N., Soonthornworasiri, N., Puduang, S., & Kwanbunjan, K. (2017) · BMC Endocrine Disorders · doi:10.1186/s12902-017-0189-z · Cited by 107
Echeverría, F., Valenzuela, R., Espinosa, A., Bustamante, A., Álvarez, D., Gonzalez‐Mañán, D., Ortíz, M., Soto-Alarcón, S. A., & Videla, L. A. (2018) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2018.09.012 · Cited by 108
Kim, J. I., Kim, J. C., Kang, M. J., Lee, M. S., Kim, J. J., & Cha, I. J. (2004) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1602081 · Cited by 108
Effects of weight loss, induced by gastric bypass surgery, on HDL remodeling in obese women
Asztalos, B. F., Swarbrick, M. M., Schaefer, E. J., Dallal, G. E., Horvath, K. V., Ai, M., Stanhope, K. L., Austrheim-Smith, I. T., Wolfe, B. M., Ali, M., et al. (2010) · Journal of Lipid Research · doi:10.1194/jlr.p900015 · Cited by 108
Functional and Morphological Vascular Changes in Pediatric Nonalcoholic Fatty Liver Disease
Pacifico, L., Anania, C., Martino, F., Cantisani, V., Pascone, R., Marcantonio, A., & Chiesa, C. (2010) · Hepatology · doi:10.1002/hep.23890 · Cited by 107
Costa, C. D. S., Hammes, T. O., Rohden, F., Margis, R., Bortolotto, J. W., Padoin, A. V., Mottin, C. C., & Guaragna, R. M. V. D. C. (2009) · Obesity Surgery · doi:10.1007/s11695-009-0052-z · Cited by 108
Kobayashi, H., Nakamura, T., Miyaoka, K., Nishida, M., Funahashi, T., Yamashita, S., & Matsuzawa, Y. (2001) · Japanese Circulation Journal-english Edition · doi:10.1253/jcj.65.193 · Cited by 105
Vimaleswaran, K. S., Radha, V., Ghosh, S., Majumder, P. P., Deepa, R., Babu, H. N. S., Rao, M. R. S., & Mohan, V. (2005) · Diabetic Medicine · doi:10.1111/j.1464-5491.2005.01709.x · Cited by 106
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Chatterjee, R., Yeh, H.-C., Shafi, T., Selvin, E., Anderson, C. A. M., Pankow, J. S., Miller, E., & Brancati, F. L. (2010) · Archives of Internal Medicine · doi:10.1001/archinternmed.2010.362 · Cited by 112
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Geroldi, C., Frisoni, G. B., Paolisso, G., Bandinelli, S., Lamponi, M., Abbatecola, A. M., Zanetti, O. O., Guralnik, J. M., & Ferrucci, L. (2005) · Archives of Neurology · doi:10.1001/archneur.62.7.1067 · Cited by 112
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Nelson, M. D., Widman, L. M., Abresch, R. T., Stanhope, K. L., Havel, P. J., Styne, D. M., & McDonald, C. M. (2007) · Journal of Spinal Cord Medicine · doi:10.1080/10790268.2007.11754591 · Cited by 107
Salazar, M. R., Carbajal, H. A., Espeche, W. G., Aizpurúa, M., Sisnieguez, C. E. L., March, C., Balbín, E., Stavile, R. N., & Reaven, G. M. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12036 · Cited by 107
Krebs, N. F., Gao, D., Gralla, J., Collins, J. S., & Johnson, S. L. (2010) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2010.02.010 · Cited by 113
Jemsek, J. G., Arathoon, E., Arlotti, M., Perez, C., Sosa, N., Pokrovskiy, V., Thiry, A., Soccodato, M., Noor, M., & Giordano, M. (2005) · Clinical Infectious Diseases · doi:10.1086/498505 · Cited by 107
Blum, C. A., Müller-Stich, B. P., Huber, P., Kraenzlin, M., Schindler, C. J. A., Geyter, C. D., Keller, U. B., & Puder, J. J. (2005) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2005-0231 · Cited by 107
Krekoukia, M., Nassis, G. P., Psarra, G., Skenderi, K., Chrousos, G. P., & Sidossis, L. S. (2007) · Metabolism · doi:10.1016/j.metabol.2006.09.014 · Cited by 107
Ylönen, K., Saloranta, C., Kronberg‐Kippilä, C., Groop, L., Aro, A., & Virtanen, S. M. (2003) · Diabetes Care · doi:10.2337/diacare.26.7.1979 · Cited by 106
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Jall, S., Sachs, S., Clemmensen, C., Finan, B., Neff, F., DiMarchi, R. D., Tschöp, M. H., Müller, T. D., & Hofmann, S. M. (2017) · Molecular Metabolism · doi:10.1016/j.molmet.2017.02.002 · Cited by 107
Effect of high-intensity exercise on aged mouse brain mitochondria, neurogenesis, and inflammation
E, L., Burns, J. M., & Swerdlow, R. H. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.05.033 · Cited by 106
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Bagust, A., & Beale, S. (2003) · QJM: An International Journal of Medicine · doi:10.1093/qjmed/hcg040 · Cited by 105
Ashraf, A. P., Alvarez, J. A., Saenz, K. H., Gower, B. A., McCormick, K. L., & Franklin, F. A. (2009) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2009-0445 · Cited by 106
Takada, J., Machado, M. A., Peres, S. B., Brito, L. C., Borges‐Silva, C. N., da Costa, C. E. M., Fonseca‐Alaniz, M. H., Andreotti, S., & Lima, F. B. (2007) · Metabolism · doi:10.1016/j.metabol.2006.05.021 · Cited by 106
Yılmaz, M., ̇, A. B. ̇., Bukan, N. C., Karakoç, A., Sancak, B., Törüner, F. B., & Paşaoğlu, H. (2005) · Gynecological Endocrinology · doi:10.1080/09513590400027265 · Cited by 106
Corella, D., Tai, E. S., Sorlí, J. V., Chew, S. K., Coltell, Ó., Sotos‐Prieto, M., García-Rios, A., Estruch, R., & Ordovás, J. M. (2010) · International Journal of Obesity · doi:10.1038/ijo.2010.187 · Cited by 106
Insulin Resistance, Metabolic Syndrome, and Subclinical Atherosclerosis
Bertoni, A. G., Wong, N. D., Shea, S., Ma, S., Liu, K., Srikanthan, P., Jacobs, D. R., Wu, C. O., Saad, M. F., & Szklo, M. (2007) · Diabetes Care · doi:10.2337/dc07-1042 · Cited by 107
Lin, Y., Chang, P., Chang, M., & Ni, Y. (2014) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.113.079749 · Cited by 109
Perrone, F., da-Silva-Filho, A. C., Adôrno, I. F., Anabuki, N. T., Leal, F. S., Colombo, T., da Silva, B. D., Dock‐Nascimento, D. B., Damião, A. O. M. C., & de Aguilar‐Nascimento, J. E. (2011) · Nutrition Journal · doi:10.1186/1475-2891-10-66 · Cited by 109
Hypercoagulability in overweight and obese subjects who are asymptomatic for thrombotic events
Radu, C. M., Spiezia, L., Gavasso, S., Fadin, M., Woodhams, B. J., Vettor, R., Campello, E., Zabeo, E., & Simioni, P. (2014) · Thrombosis and Haemostasis · doi:10.1160/th14-02-0156 · Cited by 110
Haas, D. M., Ehrenthal, D. B., Koch, M. A., Catov, J. M., Barnes, S. E., Facco, F., Parker, C. B., Mercer, B. M., Merz, C. N. B., Silver, R. M., et al. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kwv309 · Cited by 104
MiR-155 Enhances Insulin Sensitivity by Coordinated Regulation of Multiple Genes in Mice
Lin, X., Qin, Y., Jia, J.-S., Lin, T., Lin, X., Chen, L., Zeng, H., Han, Y., Wu, L., Huang, S., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006308 · Cited by 107
Hypertension, Insulin, and Proinsulin in Participants With Impaired Glucose Tolerance
The Diabetes Prevention Program Research Group (2002) · Hypertension · doi:10.1161/01.hyp.0000035706.28494.09 · Cited by 105
He, X., Gao, J., Hou, H., Qi, Z., Chen, H., & Zhang, X. (2019) · Environmental Science & Technology · doi:10.1021/acs.est.9b05131 · Cited by 106
Ditzel, E. J., Nguyen, T. Y. T., Parker, P., & Camenisch, T. D. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1409501 · Cited by 106
Metadata
- Authors
- Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C.
- Affiliations
- University of Oxford (GB)
- ORCID
- Matthews, D. R.
- Venue
- Diabetologia
- Published
- 1985-07
- Volume
- 28
- Issue
- 7
- Pages
- 412-419
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2160234571
- Cited by
- 31,502
- References
- 21
- Citations by year
- 2026: 933 · 2025: 1060 · 2024: 1240 · 2023: 1291 · 2022: 1489 · 2021: 1528 · 2020: 1424 · 2019: 1427 · 2018: 1456 · 2017: 1440 · 2016: 1481 · 2015: 1682 · 2014: 1693 · 2013: 1621 · 2012: 1518
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- HOMA, insulin resistance, beta-cell function, fasting blood glucose, fasting insulin, hyperglycaemic clamp, euglycaemic clamp, intravenous glucose tolerance test, insulin receptor binding, type 2 diabetes
- MeSH terms
- Adult, Blood Glucose, Computers, Diabetes Mellitus, Type 2, Erythrocytes, Fasting, Homeostasis, Humans, Insulin, Insulin Resistance, Islets of Langerhans, Middle Aged, Monocytes, Receptor, Insulin
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.
A language model drew the claims from the paper on 2026-10-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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.
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