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)
plainstates a cause- 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.
plainstates an association- 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.
plainstates an association- 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.
plainstates an association- 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
plainstates an association- 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.
plainstates an association- 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.
plainstates an association- 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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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]
Experimental — accuracy not yet measuredThe 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
“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 18,401–18,500 of 31,469
Polycystic ovary syndrome is associated with increased osteopontin levels
Saklamaz, A., Çalan, M., Yilmaz, O., Küme, T., Temür, M., Yildiz, N., Kasap, E., Genç, M., Yürekli, B. Ş., & Kocabaş, G. Ü. (2016) · European Journal of Endocrinology · doi:10.1530/eje-15-1074 · Cited by 15
Rousseaux, J., Duhamel, A., Dumont, J., Dallongeville, J., Molnár, D., Widhalm, K., Μanios, Y., Sjöstróm, M., Kafatos, A., Breidenassel, C., et al. (2015) · Journal of Lipid Research · doi:10.1194/jlr.m057570 · Cited by 14
Kwon, D. H., Lee, J. H., Ryu, K.-J., Park, H., & Kim, T. (2016) · Obstetrics & Gynecology Science · doi:10.5468/ogs.2016.59.1.45 · Cited by 14
Vayá, A., Rivera, L., Hernández‐Mijares, A., Bautista, D., Solá, E., Romagnoli, M., Alis, R., & Laíz, B. (2015) · Clinical Hemorheology and Microcirculation · doi:10.3233/ch-141887 · Cited by 14
Insulin Sensitivity in Prepubertal Caucasian Normal Weight Children
Masuccio, F. G., Lattanzio, F., Matera, S., Giannini, C., Chiarelli, F., & Mohn, A. (2009) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem.2009.22.8.695 · Cited by 14
Qureshi, K., & Abrams, G. A. (2016) · Clinical Obesity · doi:10.1111/cob.12132 · Cited by 14
Rissanen, A., Koskela-Koivisto, T., Hägglund, H., Koponen, A. S., Aho, J. M., Pöyhönen‐Alho, M., Tiitinen, A. E., Tikkanen, H. O., & Peltonen, J. E. (2016) · Physiological Reports · doi:10.14814/phy2.12719 · Cited by 14
Barton, J. C., Barton, J. C., Adams, P. C., & Acton, R. T. (2016) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2015.0123 · Cited by 14
Relationship between Neck Circumference and Non-Alcoholic Fatty Liver Disease in Childhood Obesity
Hatipoğlu, N., Doğan, S., Mazıcıoğlu, M. M., et al. (2016) · Journal of Clinical Research in Pediatric Endocrinology · doi:10.4274/jcrpe.2313 · Cited by 14
Association of C-reactive protein and insulin resistance in patients with chronic spinal cord injury
Huang, C., Liu, C., Weng, M., Chen, T., & Huang, M. (2008) · Journal of Rehabilitation Medicine · doi:10.2340/16501977-0264 · Cited by 15
Salas-Fernández, A., Maldonado‐Hernández, J., Martínez-Basila, A., et al. (2014) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2014-0412 · Cited by 14
Shen, H., & Liangpunsakul, S. (2016) · Journal of Clinical Gastroenterology · doi:10.1097/mcg.0000000000000503 · Cited by 13
Association of Serum YKL-40 With the Presence and Severity of Obstructive Sleep Apnea Syndrome
Li, W., Yu, Z., & Jiang, C. (2014) · Laboratory Medicine · doi:10.1309/lms98akcxrsl2xor · Cited by 14
Domingo, P., Gutiérrez, M. D. M., Gallego-Escuredo, J. M., et al. (2014) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000000205 · Cited by 14
Celık, T., Yüksel, U. Ç., Demırkol, S., Buğan, B., Iyısoy, A., Kabul, H. K., Kılıç, S., Fıcı, F., & Yaman, H. (2011) · Blood Pressure Monitoring · doi:10.1097/mbp.0b013e3283450268 · Cited by 14
García-Calzón, S., Moleres, A., Gómez‐Martínez, S., Díaz, L. E., Bueno, G., Campoy, C., Martínez, J. A., Marcos, A., Azcona, C., Zalba, G., et al. (2016) · Pediatric Obesity · doi:10.1111/ijpo.12136 · Cited by 14
Effect of Hypertriglyceridemia on Beta Cell Mass and Function in ApoC3 Transgenic Mice
Liu, Y., Cheng, X., Zhang, T., Lee, S., Yamauchi, J., Xiao, X., Gittes, G. K., Qu, S., Jiang, C., & Dong, H. H. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.707885 · Cited by 14
Relationship Between Functional Exercise Capacity and Lung Functions in Obese Chidren
Özgen, İ. T., Çakır, E., Torun, E., et al. (2015) · Journal of Clinical Research in Pediatric Endocrinology · doi:10.4274/jcrpe.1990 · Cited by 14
Panikar, V., Joshi, S., Bukkawar, A., Nasikkar, N., & Santvana, C. (2007) · PubMed · Cited by 14
Association of insulin resistance with serum ferritin and aminotransferases-iron hypothesis
Huang, J. (2015) · World Journal of Experimental Medicine · doi:10.5493/wjem.v5.i4.232 · Cited by 14
Mazzuca, P., Montesi, L., Mazzoni, G., Grazzi, G., Micheli, M. M., Piergiovanni, S., Pazzini, V., Forlani, G., Latessa, P. M., & Marchesini, G. (2016) · Internal and Emergency Medicine · doi:10.1007/s11739-016-1506-7 · Cited by 15
Monfort‐Pires, M., & Ferreira, S. R. G. (2016) · Nutrition · doi:10.1016/j.nut.2016.07.021 · Cited by 14
Zhong, M., Liu, S., Zhang, G., Zhang, X., Liu, T., & Hu, S. (2016) · World Journal of Gastroenterology · doi:10.3748/wjg.v22.i29.6706 · Cited by 14
One-year effects of two intensive inpatient treatments for severely obese children and adolescents
Makkes, S., Renders, C. M., Bosmans, J. E., van der Baan‐Slootweg, O. H., Hoekstra, T., & Seidell, J. C. (2016) · BMC Pediatrics · doi:10.1186/s12887-016-0659-x · Cited by 14
Shen, H., Peng, J. L., Tayarachakul, S., & Liangpunsakul, S. (2016) · Journal of Investigative Medicine · doi:10.1136/jim-2016-000213 · Cited by 14
Iredahl, F., Högstedt, A., Henricson, J., Sjöberg, F., Tesselaar, E., & Farnebo, S. J. (2016) · Microcirculation · doi:10.1111/micc.12325 · Cited by 14
Chen, J., Song, J., Chen, Y., Ding, Q., AnFang, P., & Mao, L. (2016) · Nutrients · doi:10.3390/nu8100643 · Cited by 14
Elsamanoudy, A. Z., Abdalla, H. A., Hassanien, M. A., et al. (2016) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-016-0192-y · Cited by 14
Effects of Vitamin D deficiency treatment on metabolic markers in Hashimoto thyroiditis patients
Anaraki, P. V., Aminorroaya, A., Amini, M., Feizi, A., Iraj, B., & Tabatabaei, A. (2017) · Journal of Research in Medical Sciences · doi:10.4103/1735-1995.199090 · Cited by 14
Sebastiano, K. M. D., Bell, K. E., Mitchell, A. S., Quadrilatero, J., Dubin, J. A., & Mourtzakis, M. (2016) · Clinical Nutrition · doi:10.1016/j.clnu.2016.11.024 · Cited by 14
Association between sleep duration and cardiac structure in youths at risk for metabolic syndrome
Feng, D., Zhang, J., Fu, J., Wu, H., Wang, Y., Li, L., Zhao, Y., Li, M., & Gao, S. (2016) · Scientific Reports · doi:10.1038/srep39017 · Cited by 14
Is Subclinical Hypothyroidism in Patients with Polycystic Ovary Syndrome Associated with BMI?
Yasar, H. (2016) · Acta Endocrinologica (Bucharest) · doi:10.4183/aeb.2016.431 · Cited by 14
Li, Y., Ran, W., Zhang, J., Chen, S., Li, Y., Luo, D., Wang, C., & Jia, W. (2016) · Journal of Diabetes Investigation · doi:10.1111/jdi.12616 · Cited by 14
Figueiredo, M. S., Maia, L. D. A., Guarda, D. S., et al. (2016) · Journal of Functional Foods · doi:10.1016/j.jff.2016.12.021 · Cited by 14
Vitamin D supplementation, the metabolic syndrome and oxidative stress in obese children
Grunwald, T., Fadia, S., Bernstein, B. A., et al. (2016) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2016-0211 · Cited by 14
Li, J. V., Janle, E. M., & Campbell, W. W. (2017) · Nutrients · doi:10.3390/nu9010032 · Cited by 17
Prediabetes definitions and clinical outcomes
Lima, L. M. T. R. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30011-6 · Cited by 14
Eshriqui, I., Franco‐Sena, A. B., Farias, D. R., Vilela, A. A. F., Cunha, D. B., Barros, É. G., Emmett, P. M., & Kac, G. (2017) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2016.12.007 · Cited by 14
Shaat, N., Ignell, C., Katsarou, A., & Berntorp, K. E. (2017) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.13124 · Cited by 14
IRX3 is a genetic modifier for birth weight, adolescent obesity and transaminase metabolism
Liu, C., Chu, C., Zhang, J., Wu, D., Xu, D.-P., Li, P., Chen, Y., Liu, B., Pei, L., Zhang, L., et al. (2017) · Pediatric Obesity · doi:10.1111/ijpo.12214 · Cited by 14
Daneshmand, R., Kurl, S., Tuomainen, T., & Virtanen, J. K. (2017) · British Journal Of Nutrition · doi:10.1017/s000711451700054x · Cited by 15
Paris, T., George, E. S., Roberts, S. K., & Tierney, A. C. (2017) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000000890 · Cited by 15
Jung, S. Y., Ho, G. Y. F., Rohan, T. E., Strickler, H. D., Bea, J. W., Papp, J. C., Sobel, E. M., Zhang, Z., & Crandall, C. J. (2017) · Breast Cancer Research and Treatment · doi:10.1007/s10549-017-4272-y · Cited by 14
Brenner, M., Abadi, S. E. M., Balouchzadeh, R., Lee, H. F., Ko, H. S., Johns, M. S., Malik, N., Lee, J. J., & Kwon, G. (2017) · Heliyon · doi:10.1016/j.heliyon.2017.e00310 · Cited by 14
Xu, G., Yoshitomi, H., Sun, W., Guo, X., Wu, L., Guo, X., Qin, L., Fan, Y., Xu, T., Liu, T., et al. (2017) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2017/4602153 · Cited by 14
Metabolic response to three different diets in lean cats and cats predisposed to overweight
Keller, C., Liesegang, A., Frey, D., & Wichert, B. A. (2017) · BMC Veterinary Research · doi:10.1186/s12917-017-1107-3 · Cited by 18
Baek, K., Lee, N., & Chung, I. (2017) · Annals of Occupational and Environmental Medicine · doi:10.1186/s40557-017-0181-0 · Cited by 14
Shalaby, S. M., Zidan, H. E., Shokry, A., Saeed, J., & El-Sokkary, R. H. (2017) · The Journal of Gene Medicine · doi:10.1002/jgm.2973 · Cited by 14
Al-Tahami, B. A. M., Ismail, A. A. A.-S., Sanip, Z., Zurkurnai, Y., Shihabudin, T. M. T., Singh, T. S. P., & Rasool, A. H. G. (2017) · Journal of Nippon Medical School · doi:10.1272/jnms.84.125 · Cited by 15
Daimon, M., KAMBA, A., Murakami, H., Mizushiri, S., Osonoi, S., Matsuki, K., Sato, E., Tanabe, J., Takayasu, S., Matsuhashi, Y., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-10815-y · Cited by 14
Ohmi, S., Ono, M., Takata, H., Hirano, S., Funakoshi, S., Nishi, Y., Yoshimura, K., Amano, E., Terada, Y., Saibara, T., et al. (2017) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-017-0264-7 · Cited by 15
de Luis, D. A., Ballesteros-Pomar, M. D., Guzmán, A. L., et al. (2017) · Clinical Nutrition · doi:10.1016/j.clnu.2017.10.013 · Cited by 14
Pescatello, L. S., Schifano, E. D., Ash, G. I., Panza, G. A., Corso, L. M. L., Chen, M., Deshpande, V., Zaleski, A. L., Cılhoroz, B., Farinatti, P., et al. (2017) · Physiological Reports · doi:10.14814/phy2.13510 · Cited by 13
Çalışkan, Z., Mutlu, T., Güven, M., Tunçdemi̇r, M., Niyazoğlu, M., Hacioglu, Y., & Dinçer, Y. (2017) · Gene · doi:10.1016/j.gene.2017.11.071 · Cited by 14
Tapp, R. J., Hughes, A. D., Kähönen, M. A. P., et al. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.006284 · Cited by 14
Mayurasakorn, K., Hasanah, N., Homma, T., Homma, M., Rangel, I. K., Garza, A., Romero, J. R., Adler, G. K., Williams, G. H., & Pojoga, L. H. (2018) · Metabolism · doi:10.1016/j.metabol.2018.01.012 · Cited by 14
An Extract of Russian Tarragon Prevents Obesity‐Related Ectopic Lipid Accumulation
Yu, Y., Mendoza, T. M., Ribnicky, D., Poulev, A., Noland, R. C., Mynatt, R. L., Raskin, I., Cefalu, W. T., & Floyd, Z. E. (2018) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201700856 · Cited by 15
Mejía-Montilla, J., Reyna‐Villasmil, E., Domínguez-Brito, L., Naranjo-Rodríguez, C., Verdugo, D. D. N., Samaniego, M. V. P., & Vargas-Olalla, V. (2018) · Endocrinología Diabetes y Nutrición · doi:10.1016/j.endinu.2017.12.010 · Cited by 14
Burns, C. M., Kaszniak, A. W., Chen, K., Lee, W., Bandy, D. J., Caselli, R. J., & Reiman, E. M. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-170767 · Cited by 17
Haapala, E. A., Lintu, N., Eloranta, A., Venäläinen, T. M., Poikkeus, A., Ahonen, T. P. S., Lindi, V., & Lakka, T. A. (2018) · Journal of Sports Sciences · doi:10.1080/02640414.2018.1449562 · Cited by 15
Tarp, J., Jespersen, E., Møller, N. C., Klakk, H., Wessner, B., Wedderkopp, N., & Bugge, A. (2018) · BMC Public Health · doi:10.1186/s12889-018-5524-4 · Cited by 15
Venegas-Pino, D. E., Lagrotteria, A., Wang, P., Morphet, J. A. M., Clapdorp, C., Shi, Y., & Werstuck, G. H. (2018) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2018.05.044 · Cited by 15
BAJAJ, H. S., Ye, C., Hanley, A. J., Sermer, M., Zinman, B., & Retnakaran, R. R. (2018) · Diabetes and Vascular Disease Research · doi:10.1177/1479164118779924 · Cited by 15
Role of adipokines FGF21, leptin and adiponectin in self-concept of youths with obesity
Li, G., Feng, D., Qu, X., Fu, J., Wang, Y., Li, L., Li, L., Han, L., Esangbedo, I. C., Li, M., et al. (2018) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2018.05.015 · Cited by 14
Pileggi, C. A., Hedges, C. P., D’Souza, R. F., Durainayagam, B. R., Markworth, J. F., Hickey, A. J., Mitchell, C. J., & Cameron‐Smith, D. (2018) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2018.06.012 · Cited by 15
Cha, E., Talman, M. S., Massey, A. H., Yan, F., & Rogers, A. E. (2018) · Biological Research For Nursing · doi:10.1177/1099800418791047 · Cited by 14
Rádiková, Ž., Penesová, A., Vlček, M., Havranová, A., Siváková, M., Šiarnik, P., Žitňanová, I., Imrich, R., Kollár, B., & Turčáni, P. (2018) · Endocrine Regulations · doi:10.2478/enr-2018-0017 · Cited by 14
Relationship between fat distribution and cardiometabolic risk in Hispanic girls
Hetherington‐Rauth, M., Bea, J. W., Lee, V. R., Blew, R. M., Funk, J. L., Lohman, T. G., & Going, S. B. (2018) · American Journal of Human Biology · doi:10.1002/ajhb.23149 · Cited by 14
Remor, J. M., Lopes, W. A., Locateli, J. C., Oliveira, R. P., Simões, C. F., Barrero, C. A. L., & Nardo, N. (2019) · Nutrition (Burbank, Los Angeles County, Calif.) · doi:10.1016/j.nut.2018.08.017 · Cited by 16
Rubin, D. A., Duran, A. T., Haqq, A. M., Gertz, E., & Dumont-Driscoll, M. C. (2018) · Pediatric Obesity · doi:10.1111/ijpo.12462 · Cited by 14
Association of CTRP13 With Liver Enzymes and Cognitive Symptoms in Nonalcoholic Fatty Liver Disease
An, K., Starkweather, A. R., Sturgill, J., Salyer, J., & Sterling, R. K. (2018) · Nursing Research · doi:10.1097/nnr.0000000000000319 · Cited by 15
Honda, T., Kishimoto, H., Mukai, N., Hata, J., Yoshida, D., Hirakawa, Y., Shibata, M., Ohara, T., Kumagai, S., & Ninomiya, T. (2018) · Journal of Diabetes Investigation · doi:10.1111/jdi.12968 · Cited by 13
Treskes, N., Koekkoek, W. A. C., & van Zanten, A. R. H. (2018) · Shock · doi:10.1097/shk.0000000000001287 · Cited by 13
Skeletal muscle ceramides do not contribute to physical-inactivity-induced insulin resistance
Appriou, Z., Nay, K., Pierre, N., Saligaut, D., Lefeuvre‐Orfila, L., Martin, B., Cavey, T., Ropert, M., Loréal, O., Rannou‐Bekono, F., et al. (2019) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2018-0850 · Cited by 14
Jorgačević, B. Z., Vučević, D. B., Vesković, M. N., Mladenović, D. R., Vukićević, D., Ješić-Vukićević, R., Todorović, V., & Radosavljević, T. S. (2019) · Canadian Journal of Physiology and Pharmacology · doi:10.1139/cjpp-2018-0607 · Cited by 14
Thrombospondin-2 as a Potential Risk Factor in a General Population
Morikawa, N., Adachi, H., Enomoto, M., Fukami, A., Kumagai, E., Nakamura, S., Nohara, Y., Nakao, E., Kono, S., Tsuru, T., et al. (2019) · International Heart Journal · doi:10.1536/ihj.18-246 · Cited by 14
Poor glycemic control and posterior circulation ischemic stroke
Kuroda, J., Matsuo, R., Yamaguchi, Y., Sato, N., Kamouchi, M., Hata, J., Wakisaka, Y., Ago, T., Kitazono, T., & on behalf of the Fukuoka Stroke Registry Investigators (2019) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000000608 · Cited by 14
Circulating microRNA-155 is associated with insulin resistance in chronic hepatitis C patients
Samaloty, N. M. E., Hassan, Z. A., Hefny, Z. M., et al. (2019) · Arab Journal of Gastroenterology · doi:10.1016/j.ajg.2019.01.011 · Cited by 14
Jao, J. K., Jacobson, D. L., Yu, W., Borkowsky, W., Geffner, M. E., McFarland, E. J., Patel, K., Williams, P. L., Miller, T. L., & Study, F. T. P. H. C. (2019) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000002018 · Cited by 14
Zecchinati, F., Barranco, M. M., Arana, M. R., Tocchetti, G. N., Domínguez, C. J., Perdomo, V., Ruiz, M. L., Mottino, A. D., Garcı́a, F., & Villanueva, S. S. M. (2019) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2019.03.002 · Cited by 14
Sundaram, S. S., Swiderska‐Syn, M., Sokol, R. J., Halbower, A. C., Capocelli, K. E., Pan, Z., Robbins, K. N., Graham, B. B., & Diehl, A. M. (2019) · Hepatology Communications · doi:10.1002/hep4.1354 · Cited by 14
Xu, Z., Zhang, M.-Y., Ni, J., Cheng, R., Zheng, Z., Li, X., & Luo, F. (2019) · World Journal of Pediatrics · doi:10.1007/s12519-019-00243-5 · Cited by 14
Aprelini, C. M. D. O., Luft, V. C., Velásquez-Meléndez, G., Schmidt, M. I., Mill, J. G., & Molina, M. D. C. B. (2019) · Revista Panamericana de Salud Pública · doi:10.26633/rpsp.2019.40 · Cited by 17
Serum Vascular Endothelial Growth Factor in Egyptian Obese Women with Insulin Resistance
Zaki, M. E., Basha, W. A., Yousef, R. N., & Awad, M. A. M. (2019) · Open Access Macedonian Journal of Medical Sciences · doi:10.3889/oamjms.2019.156 · Cited by 14
Hyperglycemia and insulin function in antiretroviral treatment-naive HIV patients in Ethiopia
Faurholt‐Jepsen, D., Olsen, M. F., Andersen, A. B., Kæstel, P., Abdissa, A., Amare, H., Yilma, D., Girma, T., Tesfaye, M., Andersen, Å. B., et al. (2019) · AIDS · doi:10.1097/qad.0000000000002249 · Cited by 15
Abdel‐Ghaffar, A., El-Hossary, G. G., Mahmoud, A. M., Elshazly, A. H. M., Hassanin, O. A., Saleh, A., Mansour, S. M., Metwally, F. G., Hanafy, L. K., Karam, S. H., et al. (2019) · Bulletin of the National Research Centre/Bulletin of the National Research Center · doi:10.1186/s42269-019-0089-7 · Cited by 14
Enhörning, S., & Malan, L. (2019) · Endocrine · doi:10.1007/s12020-019-01977-y · Cited by 14
de Sousa, M. V., Fukui, R. T., Dagogo-Jack, S. E., Krustrup, P., Zouhal, H., & da Silva, M. E. R. (2019) · Physiology & Behavior · doi:10.1016/j.physbeh.2019.112618 · Cited by 15
Carpéné, C., Mauriège, P., Boulet, N., Biron, S., Grolleau, J., García-Barrado, M., & Iglesias-Osma, M. (2019) · Medicines · doi:10.3390/medicines6030089 · Cited by 13
Rancourt, R. C., Ott, R., Schellong, K., Melchior, K., Ziska, T., Henrich, W., & Plagemann, A. (2019) · Adipocyte · doi:10.1080/21623945.2019.1675239 · Cited by 14
Altered Expression of Adrenomedullin 2 and its Receptor in the Adipose Tissue of Obese Patients
Kim, J., Lee, S. K., Kim, D.-U., Choe, H., Jang, Y. J., Park, H. S., Kim, J.-H., Hong, J. P., Lee, Y. J., & Heo, Y. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgz066 · Cited by 16
Jackson, R. M., Griesel, B. A., Short, K. R., et al. (2019) · Obesity · doi:10.1002/oby.22652 · Cited by 14
Cockcroft, E., Bond, B., Williams, C. A., Harris, S., Jackman, S. R., Armstrong, N., & BARKER, A. R. (2019) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-019-0141-9 · Cited by 14
Motta, B. M., Grander, C., Gögele, M., Foco, L., Vuković, V., Melotti, R., Fuchsberger, C., Grandi, A. D., Cantaloni, C., Picard, A., et al. (2019) · Journal of Translational Medicine · doi:10.1186/s12967-019-02130-z · Cited by 16
Koval, S. M., Snihurska, I. O., Старченко, Т. Г., Penkova, M. Y., Mysnychenko, O. V., Yushko, K. O., Lytvynova, O. M., Vysotska, O. V., & Berezin, A. Е. (2019) · Biomedical Research and Therapy · doi:10.15419/bmrat.v6i11.578 · Cited by 14
Iwalokun, B., Udoh, B. E., Etukumana, E. A., & Amoo, J. (2019) · Saudi Journal of Medicine and Medical Sciences · doi:10.4103/sjmms.sjmms_178_18 · Cited by 15
Newbouldia laevis improved glucose and fat homeostasis in a TYPE-2 diabesity mice model
Mbagwu, I. S., Akah, P. A., & Ajaghaku, D. L. (2020) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2020.112555 · Cited by 16
Lindsay, K. L., Buß, C., Wadhwa, P. D., & Entringer, S. (2020) · Nutrients · doi:10.3390/nu12020420 · Cited by 17
Shi, Y., Sun, M., Wang, Z., Hsu, H.-T., Shen, M., Yang, T., & Fu, Q. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108080 · Cited by 15
Plasma biomarkers of small intestine adaptations in obesity-related metabolic alterations
Lalande, C., Drouin‐Chartier, J., Tremblay, A. J., et al. (2020) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-020-00530-6 · Cited by 14
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
