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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
15 Own result · 7 Own interpretation · 3 No full text of the sources · 1 Partly supported · 1 No basis given · 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)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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 (%)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Subjects and procedures
“The same 12 normal subjects and 11 Type 2 diabetic patients came to the hospital fasting on a separate day for a continuous infusion”
§ 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
“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
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 5,001–5,100 of 31,469
Cristi‐Montero, C., Chillón, P., Labayen, I., Casajús, J. A., González‐Gross, M., Vanhelst, J., Μanios, Y., Moreno, L. A., Ortega, F. B., & Ruiz, J. R. (2018) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2018.03.004 · Cited by 70
Plasma levels of free fatty acids correlate with type 2 diabetes mellitus
Spiller, S., Blüher, M., & Hoffmann, R. (2018) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13449 · Cited by 72
Kahleová, H., Dort, S., Holubkov, R., & Barnard, N. D. (2018) · Nutrients · doi:10.3390/nu10091302 · Cited by 70
Wijarnpreecha, K., Kim, D., Raymond, P., Scribani, M., & Ahmed, A. (2019) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000001397 · Cited by 74
Lim, S.-M., Lee, H. S., Jung, J. I., Kim, S. M., Kim, N. Y., Seo, T. S., Bae, J.-S., & Kim, E. J. (2019) · Nutrients · doi:10.3390/nu11051190 · Cited by 72
Mohamud, W. N. W., Ismail, A. A.-S., Sharifuddin, A., Ismail, I. S., Musa, K. I., Kadir, K. A., Kamaruddin, N. A., Yaacob, N. A., Mustafa, N., Ali, O., et al. (2010) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2010.11.025 · Cited by 87
Abnormal Glucose Regulation Is Associated With Lipid-Rich Coronary Plaque
Amano, T., Matsubara, T., Uetani, T., Nanki, M., Marui, N., Kato, M., Yoshida, T., Arai, K., Yokoi, K., Ando, H., et al. (2008) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2007.09.003 · Cited by 72
Role of Insulin Resistance in Nondipper Essential Hypertensive Patients
Anan, F., Takahashi, N., Tatsuhiko, O. O. I. E., Yufu, K., Saikawa, T., & Yoshimatsu, H. (2003) · Hypertension Research · doi:10.1291/hypres.26.669 · Cited by 71
Abatacept Improves Whole-Body Insulin Sensitivity in Rheumatoid Arthritis
Ursini, F., Russo, E., Hribal, M. L., Mauro, D., Savarino, F., Bruno, C., Tripolino, C., Rubino, M., Naty, S., & Grembiale, R. D. (2015) · Medicine · doi:10.1097/md.0000000000000888 · Cited by 69
Cho, N. H., Lim, S. K., Jang, H. C., Park, H. K., & Metzger, B. E. (2005) · Diabetes Care · doi:10.2337/diacare.28.11.2750 · Cited by 71
Goldstein, B. J., Rosenstock, J., Anzalone, D. A., Tou, C., & Öhman, K. P. (2006) · Current Medical Research and Opinion · doi:10.1185/030079906x154169 · Cited by 70
Owen, K. R., Donohoe, M. E., Ellard, S., & Hattersley, A. T. (2003) · Diabetic Medicine · doi:10.1046/j.1464-5491.2003.01034.x · Cited by 69
Valeille, K., Gripois, D., Blouquit, M.-F., Souidi, M., Riottot, M., Bouthegourd, J.-C., Sérougne, C., & Martin, J. (2004) · British Journal Of Nutrition · doi:10.1079/bjn20031057 · Cited by 71
Liu, P. Y., Wishart, S. M., Celermajer, D., Jimenez, M., di Pierro, I., Conway, A., & Handelsman, D. (2003) · European Journal of Endocrinology · doi:10.1530/eje.0.1480055 · Cited by 69
Milward, A., Metherell, L., Maamra, M., et al. (2004) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2003-031418 · Cited by 70
Derosa, G., Franzetti, I. G., Querci, F., Carbone, A., Ciccarelli, L., Piccinni, M. N., Fogari, E., & Maffioli, P. (2012) · Diabetic Medicine · doi:10.1111/j.1464-5491.2012.03699.x · Cited by 70
Teede, H. J., Meyer, C., Hutchison, S. K., Zoungas, S., McGrath, B. P., & Moran, L. J. (2010) · Fertility and Sterility · doi:10.1016/j.fertnstert.2008.09.034 · Cited by 68
A Neural Circuitry Linking Insulin Resistance to Depressed Mood
Ryan, J. P., Sheu, L. K., Critchley, H. D., & Gianaros, P. J. (2012) · Psychosomatic Medicine · doi:10.1097/psy.0b013e31824d0865 · Cited by 68
Francischetti, E. A., Tibirica, E., da Silva, E. G., Rodrigues, E., Celoria, B. M., & de Abreu, V. G. (2011) · Microvascular Research · doi:10.1016/j.mvr.2011.01.002 · Cited by 69
Vargas, V., Allende, H., Lecube, A., Salcedo, M. T., Baena-Fustegueras, J. A., Fort, J. M., Rivero, J., Ferrer, R., Catalán, R., Pardina, E., et al. (2012) · World Journal of Hepatology · doi:10.4254/wjh.v4.i12.382 · Cited by 69
Yoshimura, A., Ohnishi, S., Orito, C., Kawahara, Y., Takasaki, H., Takeda, H., Sakamoto, N., & Hashino, S. (2015) · Obesity Facts · doi:10.1159/000373881 · Cited by 69
Huang, M., Wang, P., Xu, S., Xu, W., Xu, W., Chu, K., & Lu, J. (2015) · Pharmaceutical Biology · doi:10.3109/13880209.2014.959611 · Cited by 69
Lara‐Riegos, J., Ortiz‐López, M. G., Peña‐Espinoza, B. I., Montúfar‐Robles, I., Peña-Rico, M. Á., Sanchez-Pozos, K. A., Granados‐Silvestre, M. D. L. Á., & Menjívar, M. (2015) · Gene · doi:10.1016/j.gene.2015.03.065 · Cited by 71
Bailey‐Downs, L. C., Sosnowska, D., Tóth, P., Mitschelen, M. C., Gautam, T., Henthorn, J., Ballabh, P., Koller, Á., Farley, J. A., Sonntag, W. E., et al. (2011) · The Journals of Gerontology Series A · doi:10.1093/gerona/glr197 · Cited by 69
High-fat diet and age-dependent effects on enteric glial cell populations of mouse small intestine
Stenkamp-Strahm, C. M., Patterson, S., Boren, J., Gericke, M. T., & Balemba, O. B. (2013) · Autonomic Neuroscience · doi:10.1016/j.autneu.2013.04.014 · Cited by 72
Trahair, L. G., Horowitz, M., Rayner, C. K., Gentilcore, D., Lange, K. M., Wishart, J. M., & Jones, K. L. (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-2583 · Cited by 68
ACE2 deficiency reduces β-cell mass and impairs β-cell proliferation in obese C57BL/6 mice
Shoemaker, R., Yiannikouris, F. B., Thatcher, S. E., & Cassis, L. A. (2015) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00054.2015 · Cited by 70
Dang, J., Yang, M., Zhang, X., Ruan, H., Qin, G., Fu, J., Shen, Z., Tan, A., Li, R., & Moore, J. B. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15112593 · Cited by 72
Poggiogalle, E., Lubrano, C., Gnessi, L., Mariani, S., Martino, M. D., Catalano, C., Lenzi, A., & Donini, L. M. (2019) · Clinical Nutrition · doi:10.1016/j.clnu.2019.01.028 · Cited by 66
Wang, D., Li, C., Fan, W., et al. (2019) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2019.04.160 · Cited by 69
Low-Frequency HIIT Improves Body Composition and Aerobic Capacity in Overweight Men
Chin, E. C., Yu, A. P., Lai, C. W. K., Fong, D., Chan, D. K. C., Wong, S. H., Sun, F., Ngai, H. H. Y., Yung, P. S., & Parco Ming-fai, S. I. U. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002097 · Cited by 74
Fidélix, M. P., Milenković, D., Sivieri, K., & Cesar, T. B. (2020) · Food & Function · doi:10.1039/c9fo02623a · Cited by 70
Castaldo, G., Rastrelli, L., Galdo, G., Molettieri, P., Aufiero, F. R., & Cereda, E. (2020) · Nutrition · doi:10.1016/j.nut.2020.110757 · Cited by 69
Yılmaz, M. İ., Romano, M., Başaralı, M. K., Elzagallaai, A. A., Karaman, M., Demir, Z., Demir, M. F., Akçay, F., Seyrek, M., Haksever, N., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-65528-6 · Cited by 69
Tan, M., Cheng, Y., Zhong, X., Yang, D., Jiang, S., Ye, Y., Ding, M., Guan, G., Yang, D., & Zhao, X. (2021) · Aging · doi:10.18632/aging.202421 · Cited by 69
Reduced Lysosomal Acid Lipase Activity in Adult Patients With Non-alcoholic Fatty Liver Disease
Baratta, F., Pastori, D., Ben, M. D., Polimeni, L., Labbadia, G., Santo, S. D., Piemonte, F., Tozzi, G., Violi, F., & Angelico, F. (2015) · EBioMedicine · doi:10.1016/j.ebiom.2015.05.018 · Cited by 69
Suppressed Bone Turnover in Obesity: A Link to Energy Metabolism? A Case-Control Study
Viljakainen, H. T., Ivaska, K. K., Paldánius, P. M., Lipsanen‐Nyman, M., Saukkonen, T. T., Pietiläinen, K. H., ANDERSSON, S. M., Laitinen, K., & Mäkitie, O. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-3097 · Cited by 71
Ren, Q., Han, X., Wang, F., Zhang, X. Y., Han, L., Luo, Y., Zhou, X., & Ji, L. (2008) · Diabetologia · doi:10.1007/s00125-008-1039-3 · Cited by 69
Fontana, L., Meyer, T. E., Klein, S., & Holloszy, J. O. (2007) · Rejuvenation Research · doi:10.1089/rej.2006.0529 · Cited by 70
Yabe, D., Kuroe, A., Watanabe, K., Iwasaki, M., Hamasaki, A., Hamamoto, Y., Harada, N., Yamane, S., Lee, S., Murotani, K., et al. (2014) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2014.12.010 · Cited by 69
Plasma selenium levels in Turkish women with polycystic ovary syndrome
Coşkun, A., Arıkan, T. A., Kılınç, M., Arıkan, D. C., & Ekerbiçer, H. Ç. (2013) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2013.01.021 · Cited by 69
Appetite hormones and the transition to hyperphagia in children with Prader-Willi syndrome
Goldstone, A. P., Holland, A. J., Butler, J., & Whittington, J. E. (2012) · International Journal of Obesity · doi:10.1038/ijo.2011.274 · Cited by 69
Polycystic ovary syndrome and hyperprolactinemia are distinct entities
Filho, R. B., Domingues, L., Naves, L. A., FERRAZ, E. M., Alves, A. S., & Casulari, L. A. (2007) · Gynecological Endocrinology · doi:10.1080/09513590701297708 · Cited by 68
Vix, M., Diana, M., Liu, K.-H., D’Urso, A., Mutter, D., Wu, H., & Marescaux, J. (2012) · Obesity Surgery · doi:10.1007/s11695-012-0827-5 · Cited by 69
Pardina, E., Ferrer, R., Baena‐Fustegueras, J. A., Déniz, J. R., Lecube, A., Fort, J. M., Vargas, V., Catalán, R., & Peinado‐Onsurbe, J. (2011) · Obesity Surgery · doi:10.1007/s11695-011-0546-3 · Cited by 69
Insulin sensitivity and hyperprolactinemia
Tuzcu, A. K., Bahçeci, M., Dursun, M., et al. (2003) · Journal of Endocrinological Investigation · doi:10.1007/bf03345182 · Cited by 69
Jung, C. H., Lee, M. J., Kang, Y. M., Hwang, J. Y., Jang, J. E., Leem, J., Park, J., Kim, H., & Lee, W. J. (2013) · Metabolism · doi:10.1016/j.metabol.2013.09.011 · Cited by 68
Impact of peripheral glucoregulation on memory.
Awad, N., Gagnon, M., Desrochers, A., Tsiakas, M., & Messier, C. (2002) · Behavioral Neuroscience · doi:10.1037/0735-7044.116.4.691 · Cited by 69
Lee, A., Jang, H. B., Ra, M., Choi, Y., Lee, H., Park, J. Y., Kang, J., Park, K., Park, S. I., & Song, J. (2014) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2014.10.220 · Cited by 69
Associations between the number of natural teeth and metabolic syndrome in adults
Zhu, Y., & Hollis, J. (2015) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.12361 · Cited by 69
Rhee, E., Oh, K. W., Lee, W., Kim, S. Y., Oh, E. S., Baek, K. H., Kang, M. I., & Kim, S. W. (2005) · Archives of Medical Research · doi:10.1016/j.arcmed.2005.04.008 · Cited by 69
Serum Fetuin-A Concentration and Endothelial Dysfunction in Chronic Kidney Disease
Çağlar, K., Yılmaz, M. İ., Sağlam, M., Çakır, E., Kılıç, S., Sönmez, A., Eyileten, T., Yenicesu, M., Oğuz, Y., Taşar, M., et al. (2008) · Nephron Clinical Practice · doi:10.1159/000120209 · Cited by 69
Guaraldi, G., Stentarelli, C., Zona, S., Orlando, G., Carli, F., Ligabue, G., Lattanzi, A., Zaccherini, G., Rossi, R., Modena, M. G., et al. (2009) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2009.06.011 · Cited by 69
Maggio, M. G., Lauretani, F., Ceda, G. P., Bandinelli, S., Basaria, S. S., Paolisso, G., Blè, A., Egan, J. M., Metter, E. J., Abbatecola, A. M., et al. (2006) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00339.2006 · Cited by 69
Klein, S., Fabbrini, E., Patterson, B. W., Polonsky, K. S., Schiavon, C. A., Correa, J. L., Salles, J. E. N., WAJCHENBERG, B. L., & COHEN, R. V. (2012) · Obesity · doi:10.1038/oby.2011.377 · Cited by 69
Cardiorespiratory Fitness and Clustered Cardiovascular Disease Risk in U.S. Adolescents
Lobelo, F., Pate, R. R., Dowda, M. C., Liese, A. D., & Daniels, S. R. (2010) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2010.04.012 · Cited by 71
Xiao, Y., Xu, A., Hui, X. H., Zhou, P., Li, X., Zhong, H., Tang, W., Huang, G., & Zhou, Z. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066607 · Cited by 70
Endurance Training Per Se Increases Metabolic Health in Young, Moderately Overweight Men
Nordby, P., Auerbach, P., Rosenkilde, M., Kristiansen, L., Thomasen, J. R., Rygaard, L., Groth, R., Brandt, N., Helge, J. W., Richter, E. A., et al. (2012) · Obesity · doi:10.1038/oby.2012.70 · Cited by 69
Patterns of Adiponectin Expression in Term Pregnancy: Impact of Obesity
Haghiac, M., Basu, S., Presley, L. H., Serre, D., Catalano, P. M., & Mouzon, S. H. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-4074 · Cited by 70
The Metabolic Profile in Active Acromegaly is Gender-Specific
Ciresi, A., Amato, M. C., Pivonello, R., Nazzari, E., Grasso, L. F. S., Minuto, F. M., Ferone, D., Colao, A., & Giordano, C. S. D. S. (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2012-2896 · Cited by 70
Williams, R. M., Deeb, A., Ong, K. K., Bich, W., Murgatroyd, P. R., Hughes, I. A., & Acerini, C. L. (2009) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2009.03587.x · Cited by 68
Barker, G., Lim, R., Georgiou, H. M., & Lappas, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0042943 · Cited by 69
Fasting plasma insulin and the default mode network in women at risk for Alzheimer's disease
Kenna, H. A., Hoeft, F., Kelley, R. G., Wroolie, T., DeMuth, B. N., Reiss, A. L., & Rasgon, N. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.06.006 · Cited by 69
Lawlor, D. A., Smith, G. D., & Ebrahim, S. (2004) · Journal of Epidemiology & Community Health · doi:10.1136/jech.58.2.120 · Cited by 69
Metabolic adaptations to chronic glucose infusion in rats
Topp, B. G., McArthur, M. D., & Finegood, D. T. (2004) · Diabetologia · doi:10.1007/s00125-004-1493-5 · Cited by 69
Sönmez, A., Yılmaz, M. İ., Sağlam, M., Ünal, H. U., Gök, M., Çetinkaya, H., Karaman, M., Haymana, C., Eyileten, T., Oğuz, Y., et al. (2015) · Lipids in Health and Disease · doi:10.1186/s12944-015-0031-4 · Cited by 69
Delgado‐Cruzata, L., Zhang, W., McDonald, J. A., Tsai, W. Y., Valdovinos, C., Falci, L., Wang, Q., Crew, K. D., Santella, R. M., Hershman, D. L., et al. (2015) · Journal of Nutrition · doi:10.3945/jn.114.202853 · Cited by 69
Palou, M., Priego, T., Sánchez, J., Palou, A., & Picó, C. (2010) · Nutrition & Metabolism · doi:10.1186/1743-7075-7-69 · Cited by 69
Sánchez‐Infantes, D., Muguerza, B., Moulay, L., Hernández, R., Miguel, M., & Aleixandre, A. (2008) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf703598j · Cited by 69
Aygün, C., Kocaman, O., Şahı̇n, T., Uraz, S., Eminler, A. T., Çelebi, A., Şentürk, Ö., & Hülagü, S. (2007) · Digestive Diseases and Sciences · doi:10.1007/s10620-007-9998-7 · Cited by 69
Yang, S., Li, X., Zhang, W., Liu, C., Zhang, H., Lin, J., Yan, B., Yu, Y., Shi, X., Li, C., et al. (2012) · Nutrition in Clinical Practice · doi:10.1177/0884533611436173 · Cited by 68
Patel, D. A., Srinivasan, S. R., Xu, J.-H., Chen, W., & Berenson, G. S. (2007) · Metabolism · doi:10.1016/j.metabol.2007.01.010 · Cited by 69
Mortensen, M., Ehrmann, D. A., Littlejohn, E., & Rosenfield, R. L. (2009) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2008-2771 · Cited by 69
Oliver, P., Reynés, B., Caimari, A., & Palou, A. (2013) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-013-1246-8 · Cited by 69
Hepatic steatosis does not cause insulin resistance in people with familial hypobetalipoproteinaemia
Visser, M. E., Lammers, N. M., Nederveen, A. J., van der Graaf, M., Heerschap, A., Ackermans, M. T., Sauerwein, H. P., Stroes, E. S. G., & Serlie, M. J. M. (2011) · Diabetologia · doi:10.1007/s00125-011-2157-x · Cited by 71
Martínez‐Hervás, S., Vinué, Á., Núñez, L., Andrés‐Blasco, I., Piqueras, L., Real, J. T., Ascaso, J. F., Burks, D. J., Sanz, M., & Navarro, H. G. (2014) · Cardiovascular Research · doi:10.1093/cvr/cvu115 · Cited by 70
Nakajima, T., Moriguchi, M., Katagishi, T., Sekoguchi, S., Nishikawa, T., Takashima, H., Kimura, H., Minami, M., Itoh, Y., Kagawa, K., et al. (2005) · Liver International · doi:10.1111/j.1478-3231.2005.01178.x · Cited by 72
Qi, Q., Wang, J., Li, H., Yu, Z., Ye, X., Hu, F. B., Franco, O. H., Pan, A., Liu, Y., & Xu, L. (2008) · European Journal of Endocrinology · doi:10.1530/eje-08-0427 · Cited by 68
Choi, J., Rhee, E., Kim, K. H., Woo, H., Lee, W., & Sung, K. (2011) · European Journal of Endocrinology · doi:10.1530/eje-11-0375 · Cited by 73
Richard, C., Couture, P., Desroches, S., Charest, A., & Lamarche, B. (2010) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2010.01.012 · Cited by 72
Masternak, M. M., Al-Regaiey, K., Lim, M. M. D. R., Bonkowski, M. S., Panici, J. A., Przybylski, G. K., & Bartke, A. (2005) · The Journals of Gerontology Series A · doi:10.1093/gerona/60.10.1238 · Cited by 69
Whitsel, E. A., Quibrera, P. M., Christ, S. L., Liao, D., Prineas, R. J., Anderson, G. L., & HEISS, G. M. (2009) · American Journal of Epidemiology · doi:10.1093/aje/kwn400 · Cited by 70
Polycystic ovaries and the polycystic ovary syndrome phenotype in women with active acromegaly
Kaltsas, G., Androulakis, I. I., Tziveriotis, K., Papadogias, D., Tsikkini, A., Makras, P., Dimitriou, K., Stathopoulou, A., & Piaditis, G. P. (2007) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2007.02987.x · Cited by 69
Abdominal obesity and circulating metabolites: A twin study approach
Bogl, L. H., Kaye, S. M., Rämö, J. T., Kangas, A. J., Soininen, P., Hakkarainen, A., Lundbom, J., Lundbom, N. M. I., Ortega‐Alonso, A., Rissanen, A., et al. (2015) · Metabolism · doi:10.1016/j.metabol.2015.10.027 · Cited by 72
Nunes-Souza, V., César-Gomes, C. J., da Fonseca, L. J. S., Guedes, G. D. S., Smaniotto, S., & Rabelo, L. A. (2016) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2016/1987960 · Cited by 70
Zheng, J., Lin, M., Fang, L., Yu, Y., Yuan, L., Jin, Y., Feng, J., Wang, L., Yang, H., Chen, W., et al. (2016) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201600230 · Cited by 70
Chng, C. L., Lim, A. Y., Tan, H. C., Kovalik, J., Tham, K. W., Bee, Y. M., Lim, W., Acharyya, S., Lai, O. F., Chong, S. C., et al. (2016) · Thyroid · doi:10.1089/thy.2015.0602 · Cited by 69
Gomes, D. L., Moehlecke, M., da Silva, F. B. L., Dutra, E. S., Schaan, B. D., & de Carvalho, K. M. B. (2016) · Obesity Surgery · doi:10.1007/s11695-016-2308-8 · Cited by 70
Santos‐Parker, J. R., Strahler, T. R., Vorwald, V. M., Pierce, G. L., & Seals, D. R. (2016) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00732.2016 · Cited by 69
Ribeiro, C., Dourado, G. K. Z. S., & Cesar, T. B. (2017) · Nutrition · doi:10.1016/j.nut.2016.12.020 · Cited by 69
Winn, N. C., Grunewald, Z. I., Liu, Y., Heden, T. D., Nyhoff, L. M., & Kanaley, J. A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170690 · Cited by 70
High-normal levels of hs-CRP predict the development of non-alcoholic fatty liver in healthy men
Lee, J.-E., Yoon, K., Ryu, S., Chang, Y., & Kim, H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172666 · Cited by 69
Gaballah, H. H., Zakaria, S. S., Mwafy, S. E., Tahoon, N., & Ebeid, A. M. (2017) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2017.05.086 · Cited by 70
Association of obesity and diabetes with thyroid nodules
Buscemi, S., Massenti, F. M., Vasto, S., Galvano, F., Buscemi, C., Corleo, D., Barile, A. M., Rosafio, G., Rini, N., & Giordano, C. S. D. S. (2017) · Endocrine · doi:10.1007/s12020-017-1394-2 · Cited by 70
Grineva, E. N., Каронова, Т. Л., Micheeva, E., Belyaeva, О. D., & Belyaeva, I. L. (2013) · Aging · doi:10.18632/aging.100582 · Cited by 69
Giannella, A., Radu, C. M., Franco, L., Campello, E., Simioni, P., Avogaro, A., de Kreutzenberg, S. V., & Ceolotto, G. (2017) · Cardiovascular Diabetology · doi:10.1186/s12933-017-0600-0 · Cited by 69
Aadland, E., Andersen, L. B., Anderssen, S. A., Resaland, G. K., & Kvalheim, O. M. (2018) · Preventive Medicine · doi:10.1016/j.ypmed.2018.08.001 · Cited by 69
Crisosto, N., de Guevara, A. L., Echiburú, B., Maliqueo, M. A., Cavada, G., Ethel Codner, D., Paez, F., & Sir‐Petermann, T. (2018) · Fertility and Sterility · doi:10.1016/j.fertnstert.2018.10.011 · Cited by 74
Wanders, A. J., Blom, W. A. M., Zock, P. L., Geleijnse, J. M., Brouwer, I. A., & Alssema, M. (2019) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2018-000585 · Cited by 69
Yoneda, M., Saito, S., Ikeda, T., Fujita, K., Mawatari, H., Kirikoshi, H., Inamori, M., Nozaki, Y., Akiyama, T., Takahashi, H., et al. (2007) · Journal of Viral Hepatitis · doi:10.1111/j.1365-2893.2006.00836.x · Cited by 69
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.
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