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 17,601–17,700 of 31,469
Rovira, A. F., Miró, N. P., Zaragoza-Jordana, M., Ferré, N., Segura, M. C., Sabench, F., & Escribano, J. (2012) · Anales de Pediatría · doi:10.1016/j.anpedi.2012.06.006 · Cited by 15
Efecto del bypass gástrico en el síndrome metabólico y en el riesgo cardiovascular
Ocón, J., García, B., Benito, P. J., Gimeno, S. R., García, R. D. L. N., & López, P. (2010) · Hispana · doi:10.3305/nh.2010.25.1.4179 · Cited by 15
The association between TNF-α and insulin resistance in euglycemic women
Walsh, J., McGowan, C. A., Byrne, J., Rath, A., & McAuliffe, F. M. (2013) · Cytokine · doi:10.1016/j.cyto.2013.07.001 · Cited by 15
de Luis, D. A., Izaola, O., Aller, R., Gómez, J. J. L., Torres, B., Diaz, G., Gomez, E., & Romero, E. (2015) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.12298 · Cited by 15
Oh, T. J., Jung, H. S., Bae, J. H., Kim, Y. G., Park, K. S., Cho, Y. M., Park, K. S., & Kim, S. Y. (2013) · Journal of Korean Medical Science · doi:10.3346/jkms.2013.28.6.881 · Cited by 15
Martínez, E., Visnegarwala, F., Grund, B., Thomas, A. J., Gibert, C. L., Shlay, J. C., Drummond, F., Pearce, D. D., Edwards, S., Reiss, P., et al. (2010) · AIDS · doi:10.1097/qad.0b013e3283333666 · Cited by 16
The effect of puberty on fat oxidation rates during exercise in overweight and normal-weight girls
Chu, L., Riddell, M. C., Schneiderman, J. E., McCrindle, B. W., & Hamilton, J. K. (2013) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00888.2013 · Cited by 15
Relationship of blood pressure and obesity with inflammatory cytokines among African Americans
DeLoach, S. S., Huan, Y., Keith, S. W., Cantarin, M. P. M., & Falkner, B. E. (2011) · Therapeutic Advances in Cardiovascular Disease · doi:10.1177/1753944711408757 · Cited by 14
Elawa, G., AoudAllah, A. M., Hasaneen, A., et al. (2011) · Saudi Medical Journal · doi:10.15537/1658-3175.5352 · Cited by 15
Chan, S., Chen, J.-H., Li, Y., & Tsai, L.-M. (2012) · Journal of Biomedical Science · doi:10.1186/1423-0127-19-100 · Cited by 14
Saukkonen, T. T., Heikkinen, S., Hakkarainen, A., Häkkinen, A., Leemput, K. V., Lipsanen‐Nyman, M., & Lundbom, N. M. I. (2010) · European Journal of Endocrinology · doi:10.1530/eje-10-0186 · Cited by 15
Minocci, A., Guzzaloni, G., Marzullo, P., et al. (2005) · Diabetes & Metabolism · doi:10.1016/s1262-3636(07)70218-5 · Cited by 15
Telford, R. D., Cunningham, R. B., Shaw, J. E., Dunstan, D. W., Lafferty, A. R. A., Reynolds, G. J., Hickman, P. E., Southcott, E., Potter, J. M., Waring, P. M., et al. (2009) · Pediatric Diabetes · doi:10.1111/j.1399-5448.2009.00513.x · Cited by 15
Statin use is not associated with liver related mortality
Younoszai, Z., Zheng, L., Stepanova, M., Erario, M., Cable, R., & Younossi, Z. M. (2014) · Annals of Hepatology · doi:10.1016/s1665-2681(19)30908-1 · Cited by 15
Roohafza, H., Shokouh, P., Sadeghi, M., Alikhassy, Z., & Sarrafzadegan, N. (2014) · International Scholarly Research Notices · doi:10.1155/2014/697617 · Cited by 15
Síndrome metabólico, diabetes mellitus tipo 1 y resistencia a la insulina
Chillarón, J. J., Goday, A., & Pedro‐Botet, J. (2008) · Medicina Clínica · doi:10.1157/13118111 · Cited by 16
Sopher, A. B., Gerken, A. T., Blaner, W. S., Root, J. M., McMahon, D. J., & Oberfield, S. E. (2012) · Fertility and Sterility · doi:10.1016/j.fertnstert.2012.01.111 · Cited by 15
Ovarian HMW adiponectin is associated with folliculogenesis in women with polycystic ovary syndrome
Tao, T., Xu, B., & Liu, W. (2013) · Reproductive Biology and Endocrinology · doi:10.1186/1477-7827-11-99 · Cited by 15
Dudzińska, W., Lubkowska, A., Jakubowska, K., et al. (2013) · Physiological Research · doi:10.33549/physiolres.932355 · Cited by 15
Gokulakrishnan, K., Amutha, A., Ranjani, H., Subramanian, B. Y., Balakumar, M., Pandey, G. K., Anjana, R. M., Ali, M. K., Narayan, K. M. V., & Mohan, V. (2015) · Endocrine Practice · doi:10.4158/ep15741.or · Cited by 15
Evers, S. S., van Vliet, A., van Vugt, B., Scheurink, A. J. W., & van Dijk, G. (2016) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2015.12.024 · Cited by 15
Lee, J., Kim, H. J., Cho, B., Park, J., Choi, H.-C., Lee, C. M., Oh, S., Kwon, H., & Heo, N. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141364 · Cited by 16
Wang, J., Lee, I., Lee, W., Lin, S.-D., Su, S., Tu, S., Tseng, Y., Lin, S., & Sheu, W. H. (2016) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2015.12.010 · Cited by 15
Yu, H., Liu, J.-C., Fan, Y.-J., Li, C., Zhang, L., Chen, X., Yue, S., Lu, W., Yang, X., & Tang, N. (2015) · International Archives of Occupational and Environmental Health · doi:10.1007/s00420-015-1071-9 · Cited by 15
Bretault, M., Laroche, S., Lacorte, J., Barsamian, C., Polak, M., Raffin‐Sanson, M., Touraine, P. A., Bouillot, J., Czernichow, S., & Carette, C. (2016) · Obesity Surgery · doi:10.1007/s11695-015-1977-z · Cited by 15
Homma, S., Kato, K., Hayashi, J., et al. (2014) · Angiology · doi:10.1177/0003319713516853 · Cited by 15
Salivary Biomarkers in Pediatric Metabolic Disease Research.
Hartman, M.-L., Goodson, J. M., Baraké, R., Alsmadi, O. A., Al‐Mutawa, S. A., Ariga, J., Soparkar, P. M., Behbehani, J., & Behbehani, K. (2016) · PubMed · Cited by 15
Matsuba, R., Imamura, M., Tanaka, Y., Iwata, M., Hirose, H., Kaku, K., Maegawa, H., Watada, H., Tobe, K., Kashiwagi, A., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154093 · Cited by 14
Ramos, J. S., Dalleck, L. C., Borrani, F., Fassett, R. G., & Coombes, J. S. (2016) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2016.04.007 · Cited by 15
Vitamin D status and insulin resistance among young obese Saudi females
Abdelkarem, H. M., Elsherif, M. A., & Gomaa, S. B. (2016) · Saudi Medical Journal · doi:10.15537/smj.2016.5.13581 · Cited by 15
Lee, B., Lee, W., Lo, S., Hsu, H., Chien, K., Chang, Y., & Chen, M.-F. (2016) · International journal of cardiac imaging · doi:10.1007/s10554-016-0912-2 · Cited by 14
Zhao, Y., Zhu, J., Song, G., & Li, X. (2015) · Europe PMC (PubMed Central) · Cited by 15
Cheong, J. N., Cuffe, J. S. M., Jefferies, A. J., Moritz, K. M., & Wlodek, M. E. (2016) · The Journal of Physiology · doi:10.1113/jp272212 · Cited by 15
Kimura, Y., Tamasawa, N., Matsumura, K., Murakami, H., Yamashita, M., Matsuki, K., Tanabe, J., Murakami, H., Matsui, J., & Daimon, M. (2016) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.33670 · Cited by 15
Lei, Q., Zhou, X., Zhou, Y., Mai, C., Hou, M., Lv, L., Duan, D.-M., Wen, J., Lin, X., Wang, P., et al. (2016) · Hypertension · doi:10.1161/hypertensionaha.116.07261 · Cited by 15
Espiau, M., Yeste, D., Noguera‐Julián, A., Soler‐Palacín, P., Fortuny, C., Ferrer, R., Comas, I., Martín‐Nalda, A., Deyà‐Martínez, À., Figueras, C., et al. (2016) · The Pediatric Infectious Disease Journal · doi:10.1097/inf.0000000000001394 · Cited by 14
Role of Versican and ADAMTS-1 in Polycystic Ovary Syndrome
Özler, S., Öztaş, E., Tokmak, A., et al. (2016) · Journal of Clinical Research in Pediatric Endocrinology · doi:10.4274/jcrpe.3414 · Cited by 15
Reid, M. J. A., Ma, Y., Scherzer, R., Price, J. C., French, A. L., Plankey, M. W., Grünfeld, C., & Tien, P. C. (2016) · AIDS · doi:10.1097/qad.0000000000001345 · Cited by 15
Insulin Resistance in Adipose Tissue but Not in Liver Is Associated with Aortic Valve Calcification
Galarza, E. J., Posadas‐Romero, C., Torres‐Tamayo, M., Medina-Urrutia, A. X., Rodas-Díaz, M. A., Posadas‐Sánchez, R., Vargas‐Alarcón, G., González-Salazar, M. D. C., Cardoso-Saldaña, G. C., & Juárez-Rojas, J. G. (2016) · Disease Markers · doi:10.1155/2016/9085474 · Cited by 16
San-Cristóbal, R., Navas‐Carretero, S., Milagro, F. I., Riezu‐Boj, J. I., Guruceaga, E., Celis-Morales, C. A., Livingstone, K. M., Brennan, L., Lovegrove, J. A., Daniel, H., et al. (2016) · Journal of Physiology and Biochemistry · doi:10.1007/s13105-017-0560-6 · Cited by 15
Rodovalho, S., Rachid, B., De-Lima-Junior, J. C., van de Sande‐Lee, S., Morari, J., Carvalho, H. M., Amorim, B. J., Tincani, Á. J., Chaim, É. A., Pareja, J. C., et al. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.152 · Cited by 15
Papandreou, D., Karavetian, M., Karabouta, Z., et al. (2017) · International Journal of Endocrinology · doi:10.1155/2017/2671692 · Cited by 15
Triglycerides and glycated hemoglobin for screening insulin resistance in obese patients
Boursier, G., Sultan, A., Molinari, N., Maı̈moun, L., Boegner, C., Picandet, M., Kuster, N., Bargnoux, A., Badiou, S., Dupuy, A., et al. (2017) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2017.12.002 · Cited by 15
Transition from pre-diabetes to diabetes and predictors of risk in Mexican-Americans
Wu, S., McCormick, J. B., Curran, J. E., & Fisher‐Hoch, S. P. (2017) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s136368 · Cited by 15
de Luis, D. A., Izaola, O., Primo, D., Aller, R., Ortolá, A., Gómez, E., & Gómez, J. J. L. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.01.003 · Cited by 15
McDonald, J. D., Chitchumroonchokchai, C., Li, J., Mah, E., Labyk, A. N., Reverri, E. J., Ballard, K. D., Volek, J. S., & Bruno, R. S. (2018) · British Journal Of Nutrition · doi:10.1017/s0007114517003610 · Cited by 15
Arentoft, J. L., Hoppe, C., Andersen, E. W., Overvad, K., & Tetens, I. (2018) · British Journal Of Nutrition · doi:10.1017/s0007114517003695 · Cited by 15
Dallak, M. A., Bin‐Jaliah, I., Albawardi, A. S., Haidara, M. A., Sakr, H. F., Eid, R. A., Hassan, W. N., & Al‐Ani, B. (2018) · Ultrastructural Pathology · doi:10.1080/01913123.2017.1422581 · Cited by 15
Raposo, L., Severo, M., & Santos, A. C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191641 · Cited by 15
Turcotte, M., Abadi, A., Peralta‐Romero, J., Suarez, F., Reddon, H., Gómez-Zamudio, J. H., Burguete-García, A. I., Cruz, M., & Meyre, D. (2018) · International Journal of Obesity · doi:10.1038/s41366-018-0055-8 · Cited by 16
Luciano‐Mateo, F., Cabré, N., Nadal, M., García‐Heredia, A., Baiges‐Gayà, G., Hernández‐Aguilera, A., Camps, J., Joven, J., & Domingo, J. L. (2018) · Journal of Trace Elements in Medicine and Biology · doi:10.1016/j.jtemb.2018.02.023 · Cited by 16
A Data-Driven Approach to Artificial Pancreas Verification and Synthesis
Kushner, T., Bortz, D. M., Maahs, D. M., & Sankaranarayanan, S. (2018) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1109/iccps.2018.00031 · Cited by 15
Panahi, S., Gallant, A. R., Tremblay, A., Përusse, L., Després, J., & Drapeau, V. (2018) · European Journal of Clinical Nutrition · doi:10.1038/s41430-018-0166-2 · Cited by 15
Shen, M., Wang, M., He, W., He, M., Qiao, N., Ma, Z., Ye, Z., Zhang, Q., Zhang, Y., Yang, Y., et al. (2018) · International Journal of Endocrinology · doi:10.1155/2018/3015854 · Cited by 15
Liang, S., Zhang, D., Qi, J., Song, X., & Xue, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-26276-w · Cited by 15
Increased cardiovascular disease risk in women with a history of recurrent miscarriage
Wagner, M. M., Beshay, M. M., Rooijakkers, S., Hermes, W., Jukema, J. W., le Cessie, S., de Groot, C. J. M., Ballieux, B. E. P. B., Lith, J. M. M. V., & Bloemenkamp, K. W. M. (2018) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.13392 · Cited by 15
Miceli, D. D., Vidal, P. N., Blatter, M. F. C., Pignataro, O. P., & Castillo, V. A. (2018) · Open Veterinary Journal · doi:10.4314/ovj.v8i2.13 · Cited by 15
Nematy, M., Emami, M., Behforouz, A., Jarahi, L., Zarifian, A., Rashidlamir, A., Rashed, M. M., Khaleghzade, H., Ghaneifar, Z., Safarian, M., et al. (2018) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/ijem.ijem_83_18 · Cited by 15
Landry, M. J., Khazaee, E., Markowitz, A. K., Vandyousefi, S., Ghaddar, R., Pilles, K. N., Asigbee, F. M., Gatto, N. M., & Davis, J. N. (2018) · Journal of Hunger & Environmental Nutrition · doi:10.1080/19320248.2018.1491367 · Cited by 15
Kasuga, Y., Miyakoshi, K., Tajima, A., Saisho, Y., Ikenoue, S., Ochiai, D., Matsumoto, T., Arata, N., Hata, K., & Tanaka, M. (2018) · Journal of Diabetes Investigation · doi:10.1111/jdi.12935 · Cited by 14
Jung, S. Y., Mancuso, N., Yu, H., Papp, J. C., Sobel, E. M., & Zhang, Z. (2018) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-18-0180 · Cited by 15
Choi, J. S. (2018) · Clinical Nutrition Research · doi:10.7762/cnr.2018.7.4.276 · Cited by 15
Aerobic fitness thresholds to define poor cardiometabolic health in children and youth
Aadland, E., Anderssen, S. A., Andersen, L. B., Resaland, G. K., Kolle, E., & Steene‐Johannessen, J. (2018) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.13330 · Cited by 15
Sucharita, S., Ashwini, V., Prabhu, J. S., Avadhany, S. T., Ayyar, V. S., & Bantwal, G. (2018) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/ijem.ijem_162_18 · Cited by 15
FURINvariant associations with postexercise hypotension are intensity and race dependent
Cılhoroz, B., Schifano, E. D., Panza, G. A., Ash, G. I., Corso, L. M. L., Chen, M., Deshpande, V., Zaleski, A. L., Farinatti, P., Santos, L. P., et al. (2019) · Physiological Reports · doi:10.14814/phy2.13952 · Cited by 15
Impaired insulin sensitivity is associated with worsening cognition in HIV-infected patients
Khuder, S. S., Chen, S., Letendre, S. L., Marcotte, T. D., Grant, I., Franklin, D. R., Rubin, L. H., Margolick, J. B., Jacobson, L. P., Sacktor, N. C., et al. (2019) · Neurology · doi:10.1212/wnl.0000000000007125 · Cited by 15
Anholm, C., Kumarathurai, P., Jürs, A., Pedersen, L. R., Nielsen, O. W., Kristiansen, O. P., Fenger, M., Holst, J. J., Madsbad, S., Sajadieh, A., et al. (2019) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-019-0438-6 · Cited by 17
Coagulation parameters predictive of polycystic ovary syndrome
Sun, Q., Yang, Y., Peng, X., Zhang, Y., Gao, Y. J., Wang, F., Zhang, Y., Feng, W., Yang, W. S., & Kang, X. (2019) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2019.06.018 · Cited by 15
Matsumoto, T., Ohnishi, H., Sato, T., Miki, T., Akasaka, H., Hanawa, N., Koyama, M., Saitoh, S., & Miura, T. (2019) · Cardiology and Therapy · doi:10.1007/s40119-019-0140-7 · Cited by 14
Gianotti, N., Muccini, C., Galli, L., et al. (2019) · Journal of Medical Virology · doi:10.1002/jmv.25541 · Cited by 15
O'Connor, S. E., Julien, P., Weisnagel, S. J., Gagnon, C., & Rudkowska, I. (2019) · Current Developments in Nutrition · doi:10.1093/cdn/nzz083 · Cited by 20
Li, M., Liu, Y., Jin, L., Zeng, N., Wang, L., Zhao, K., Lv, H., Zhang, M., Xu, W., Zhang, P., et al. (2019) · Obesity Surgery · doi:10.1007/s11695-019-04067-0 · Cited by 15
Uehara, M., Yamazaki, H., Yoshikawa, N., Souta-Kuribara, A., & Tanaka, H. (2019) · Endocrine Journal · doi:10.1507/endocrj.ej19-0205 · Cited by 15
Effects of hypertension on cerebral cortical thickness alterations in patients with type 2 diabetes
Shi, L., Cheng, Y., Xu, Y., Shen, Z., Lü, Y., Zhou, C., Jiang, L., Zhang, Y., Zhu, F., & Xu, X. (2019) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2019.107872 · Cited by 15
El‐khair, S. M. A., Ghoneim, F. M., Shabaan, D. A., & Elsamanoudy, A. Z. (2019) · Histochemistry and Cell Biology · doi:10.1007/s00418-019-01823-2 · Cited by 15
Farghaly, H. S., Metwalley, K. A., Raafat, D. M., Algowhary, M., & Saied, G. M. (2019) · Hormone Research in Paediatrics · doi:10.1159/000503287 · Cited by 15
Hua, M., Huang, J., Hu, C., Yao, T., & Lai, M. (2019) · Frontiers in Pediatrics · doi:10.3389/fped.2019.00420 · Cited by 15
Arai, T., Atsukawa, M., Tsubota, A., Kawano, T., Koeda, M., Yoshida, Y., Tanabe, T., Okubo, T., Hayama, K., Iwashita, A., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224184 · Cited by 17
Chen, X., Yan, Y., Weng, Z., Chen, C., Lv, M., Lin, Q., Du, Q., Shen, X., & Yang, L. (2019) · Journal of Diabetes Research · doi:10.1155/2019/5487962 · Cited by 16
Kulyté, A., Lundbäck, V., Lindgren, C. M., Luan, J., Lotta, L. A., Langenberg, C., Arner, P., Strawbridge, R. J., & Dahlman, I. A. (2020) · Molecular Metabolism · doi:10.1016/j.molmet.2020.01.009 · Cited by 15
Relationship of Vitamin D-Deficient Diet and Irisin, and Their Impact on Energy Homeostasis in Rats
Abulmeaty, M. M. A., Almajwal, A. M., Alam, I., Razak, S., Elsadek, M. F., Aljuraiban, G. S., Hussein, K. S., & Malash, A. M. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.00025 · Cited by 15
Sugita, Y., Ito, K., Sakurai, S., Sakai, S., & Kuno, S. (2020) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2020.107552 · Cited by 15
Kiel, I. A., Lionett, S., Parr, E. B., Jones, H., Røset, M. A., Salvesen, Ø. O., Vanky, E., & Moholdt, T. T. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-034733 · Cited by 15
He, S., Tang, M., Zhang, Z., et al. (2020) · New Journal of Chemistry · doi:10.1039/c9nj05533a · Cited by 15
Al-Jaghthmi, O. H. A., & Zeid, I. M. A. (2020) · Journal of Advanced Veterinary and Animal Research · doi:10.5455/javar.2020.g408 · Cited by 15
Fruzzetti, F., Benelli, E., Fidecicchi, T., & Tonacchera, M. (2020) · International Journal of Endocrinology · doi:10.1155/2020/2901393 · Cited by 15
Ye, D., Lou, G., Li, A., Dong, F., Chen, G., Xu, W., Liu, Y., & Hu, S. (2020) · Molecular Medicine Reports · doi:10.3892/mmr.2020.11077 · Cited by 15
Heo, J. E., Shim, J., Lee, H., & Kim, H. C. (2020) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2019.0110 · Cited by 16
Farag, M. M., Ashour, E. H., & El-Hadidy, W. F. (2020) · Dubai Medical Journal · doi:10.1159/000506899 · Cited by 15
Adiponectin and leptin levels in normal weight women with polycystic ovary syndrome
Gözüküçük, M., Gürsoy, A. Y., Destegül, E., Taşkın, S., & Şatıroğlu, H. (2020) · Hormone Molecular Biology and Clinical Investigation · doi:10.1515/hmbci-2020-0016 · Cited by 15
The relationship between patterns of insulin secretion and risks of gestational diabetes mellitus
Zhang, N., Tao, M., Li, H., Zhao, F., & Wang, F. (2020) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.13200 · Cited by 15
Yeste, D., Clemente, M., Campos-Martorell, A., Fábregas, A., Mogas, E., Soler, L., & Carrascosa, A. (2020) · Anales de Pediatría · doi:10.1016/j.anpedi.2020.04.004 · Cited by 19
Dried fruit consumption and cardiometabolic health: a randomised crossover trial
Sullivan, V. K., Petersen, K. S., & Kris‐Etherton, P. M. (2020) · British Journal Of Nutrition · doi:10.1017/s0007114520002007 · Cited by 15
Satitpatanapan, P., Jaruratanasirikul, S., & Sriplung, H. (2020) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2020-0019 · Cited by 15
Cano, A., Llauradó, G., Albert, L., Mazarico, I., Astiarraga, B., González-Sastre, M., Martínez, L., Fernández‐Veledo, S., Simó, R., Vendrell, J. J., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9072192 · Cited by 15
Bañales-Luna, M., Figueroa‐Vega, N., Marín-Aragón, C. I., Pérez-Luque, E. L., Ibarra‐Reynoso, L. D. R., Gallardo‐Blanco, H. L., López-Aguilar, I., & Malacara, J. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-67832-7 · Cited by 15
Chatterjee, A., Basu, A., Das, K., Singh, P., Mondal, D., Bhattacharya, B., Roychoudhury, S., Majumder, P. P., Chowdhury, A., & Basu, P. (2020) · Annals of Hepatology · doi:10.1016/j.aohep.2020.06.009 · Cited by 15
Time limited eating in adolescents with obesity (time LEAd): Study protocol
Vidmar, A. P., Goran, M. I., Naguib, M. N., Fink, C., Wee, C. P., Hegedus, E., Lopez, K., Gonzalez, J., & Raymond, J. K. (2020) · Contemporary Clinical Trials · doi:10.1016/j.cct.2020.106082 · Cited by 14
Child neurobiology impacts success in family-based behavioral treatment for children with obesity
Schur, E. A., Melhorn, S. J., Scholz, K., Leon, M. R. B. D., Elfers, C., Rowland, M. G., Saelens, B. E., & Roth, C. L. (2020) · International Journal of Obesity · doi:10.1038/s41366-020-0644-1 · Cited by 16
Klepochová, R., Leutner, M., Bastian, M., Krebs, M., Weber, M., Trattnig, S., Kautzky‐Willer, A., & Krššák, M. (2020) · Obesity · doi:10.1002/oby.22846 · Cited by 15
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