Design of a dual-hormone model predictive control for artificial pancreas with exercise model
Open accessAbstract
The Artificial Pancreas (AP) is a new technology for helping people with type 1 diabetes to better control their glucose levels through automated delivery of insulin and optionally glucagon in response to sensed glucose levels. In a dual hormone AP, insulin and glucagon are delivered automatically to the body based on glucose sensor measurements using a control algorithm that calculates the amount of hormones to be infused. A dual-hormone MPC may deliver insulin continuously; however, it must avoid continuous delivery of glucagon because nausea can occur from too much glucagon. In this paper, we propose a novel dual-hormone (DH) switching model predictive control and compare it with a single-hormone (SH) MPC. We extended both MPCs by integrating an exercise model and compared performance with and without the exercise model included. Results were obtained on a virtual patient population undergoing a simulated exercise event using a mathematical glucoregulatory model that includes exercise. Time spent in hypoglycemia is significantly less with the DH-MPC than the SH-MPC (p=0.0022). Additionally, including the exercise model in the DH-MPC can help prevent hypoglycemia (p <; 0.001).
AI snapshot
Written from the paper’s full text.
TL;DR
A dual-hormone switching model predictive controller (DH-MPC) with an integrated exercise model reduced hypoglycemia compared with single-hormone MPC in simulated type 1 diabetes. Including the exercise model in the controller was critical for preventing exercise-induced hypoglycemia.
Key findings
- Time spent in hypoglycemia was significantly less with DH-MPC than SH-MPC (p=0.0022); only the lower 2.5% of DH subjects went hypoglycemic, whereas some SH subjects within the 25%–75% interquartile range crossed the hypoglycemia threshold.
- Including the exercise model in the DH-MPC significantly reduced time spent in hypoglycemia (p<0.001).
- DH-Thr (switch based on fixed 85 mg/dl threshold) was optimal for controlling glucose during exercise; DH-Pred (switch based on predicted drop below 70 mg/dl) resulted in significantly higher time in hyperglycemia and median blood glucose (p<0.001), with hypoglycemia difference reported at p=0.011.
Methods
- In-silico study using a virtual patient population: 163 of 400 virtual subjects passed physiologic feasibility criteria and were simulated.
- Scenario: glucose brought to steady state 160 mg/dl at t=0, then 45 minutes of exercise starting at t=10 minutes at 60% PVO2max and 80% PAMM.
- Compared SH-MPC, DH-MPC, DH-Thr, and DH-Pred, with and without the exercise model; used Student’s t-test and Wilcoxon rank-sum test at 95% significance. MPC used a 300-minute prediction horizon, 20-minute control horizon, and constraints of 15 units/hour insulin and 50 micrograms glucagon.
Limitations
- Stated: the exercise model only models aerobic exercise; anaerobic exercise can cause a less significant glucose drop, and the exercise model was static for all subjects.
- Stated future work: no meal model was included (uG was zero in equation 1), and the approach was not evaluated in human subjects.
- Evident from the text: all results are from in-silico simulations, not clinical trials.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 1 cited sources checked · 0 supported · 0 flagged
- Findingown data
The DH-Pred strategy led to some hyperglycemia after exercise.
“DH-Pred resulted in some post-exercise hyperglycemia.”
Quote in the paperSubstantial claim - Findingown data
Adding the exercise model to the dual-hormone MPC significantly shortened the time spent in hypoglycemia (p<0.001).
“Additionally, including the exercise model in the DH-MPC can help prevent hypoglycemia (p < 0.001).”
- Also stated in: § III. Results and Discussion · plain
“The time spent in hypoglycemia was significantly less when exercise was included (p-value < 0.001).”
Quote in the paperSubstantial claim- Rests on an assumption: Exercise can cause rapid glucose drops and hypoglycemia when insulin dosing is not adjusted.
- Also stated in: § III. Results and Discussion · plain
- Findingown data
In virtual subjects, the dual-hormone MPC yielded a significantly shorter duration of hypoglycemia than the single-hormone MPC (p=0.0022).
“Time spent in hypoglycemia is significantly less with the DH-MPC than the SH-MPC (p=0.0022).”
- Also stated in: § III. Results and Discussion · plain
“The time the virtual subjects spent in hypoglycemia was significantly less for the DH (p-value = 0.0022).”
Quote in the paperSubstantial claim - Also stated in: § III. Results and Discussion · plain
- Findingown data
The time in hypoglycemia did not differ significantly between DH-Pred and DH-Thr (p=0.011).
“While time spent in hypoglycemia with the DH-Pred approach did not change significantly in comparison to the DH-Thr approach (p-value = 0.011)”
Quote in the paperSubstantial claim - Interpretationown data
The dual-hormone MPC that used a threshold and included exercise in its process model gave better results than the other tested controllers.
“In conclusion, the threshold-based dual-hormone MPC with exercise in the process model outperformed the other controlling approaches.”
Quote in the paperSubstantial claim- Rests on an assumption: Exercise can cause rapid glucose drops and hypoglycemia when insulin dosing is not adjusted.
- Interpretationown data
Including exercise in the controller's process model was essential for lowering the time spent in hypoglycemia.
“It was critical to include exercise in the controller process model so that the time spent in hypoglycemia would be reduced.”
Quote in the paperSubstantial claim- Rests on an assumption: Exercise can cause rapid glucose drops and hypoglycemia when insulin dosing is not adjusted.
- Premisecitation
Compared with aerobic exercise, anaerobic exercise can cause a smaller drop in glucose [15].
“Anaerobic exercise can result in a less significant drop in glucose compared with aerobic exercise [15].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] Yardley JE Kenny GP Perkins BA Resistance versus aerobic exercise: acute effects on glycemia in type 1 diabetes Diabetes Care 36 3 537 42 23172972 PMC3579339 source
Not checked in the free run.
- Interpretationown data
Among the tested strategies, DH-Thr is optimal for glucose control during exercise.
“Results here indicate that DH-Thr is optimal for controlling glucose during exercise.”
Quote in the paperSubstantial claim - Interpretationnone given
The exercise model represents only aerobic exercise's effect on the glucoregulatory system.
“A limitation of this study is that the exercise model only models the effect of aerobic exercise on the glucoregulatory system.”
Quote in the paperNot a substantial claim
- May combine two claims. 6
- The quote only partly states this claim. 1
- Classified as probably not a substantial claim. 1
May combine two claims.
Findingown dataUnder the single-hormone MPC, some subjects in the 25-75% interquartile range crossed the hypoglycemia threshold, while under the dual-hormone MPC only the lowest 2.5% became hypoglycemic.
“Some subjects within the 25%–75% interquartile range using the SH process model crossed the hypoglycemia threshold interquartile range, whereas subjects using the DH model generally did not go hypoglycemic (only the lower 2.5% of subjects went hypoglycemic under DH).”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningWhen the exercise model was left out, hypoglycemia occurred; when it was included, hypoglycemia was generally avoided.
“When the exercise model is not included, hypoglycemia occurs, whereas when it is included, hypoglycemia is generally avoided.”
Quote in the paperSubstantial claim- Rests on an assumption: Exercise can cause rapid glucose drops and hypoglycemia when insulin dosing is not adjusted.
May combine two claims.
Findingown dataWithin the dual-hormone MPC, glucagon delivery both prevented more subjects from becoming hypoglycemic and raised the median blood glucose across subjects.
“In DH, the glucagon delivery not only prevented more subjects from becoming hypoglycemic, but also increased the median blood glucose across the subjects.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataDH-Pred had significantly more time in hyperglycemia and a higher median blood glucose than DH-Thr (p<0.001).
“the time spent in hyperglycemia, as well as the median blood glucose, were significantly higher for DH-Pred (p-value < 0.001).”
Quote in the paperSubstantial claimMay combine two claims. The quote only partly states this claim.
Study design and conductown dataVirtual subjects were retained only if they passed four physiological feasibility tests; 163 of 400 did so.
“A total of 163 out of 400 virtual subjects passed the above criteria and were selected for our in-silico simulations.”
- Also stated in: § D. Virtual patient population · plain
“Each parameter set was then subjected to a battery of tests to determine the physiologic feasibility. If a virtual subject did not pass each of the following 4 criteria, they were excluded from further testing.”
Quote in the paperNot a substantial claim- Also stated in: § D. Virtual patient population · plain
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe exercise model was the same for all subjects, even though individuals can vary in their response to exercise.
“Furthermore, the exercise model was static for all subjects, whereas different people can respond differently to exercise.”
Quote in the paperNot a substantial claim- Rests on an assumption: Individuals can respond differently to exercise.
May combine two claims.
Interpretationown dataWithout the exercise model, the controller could not foresee exercise-induced hypoglycemia and therefore did not stop insulin or raise glucagon delivery.
“When the exercise model is not included, the algorithm cannot anticipate forthcoming exercise-induced hypoglycemia and thereby turn off insulin and increase glucagon dosing as shown in the insulin and glucagon delivery panels of Figure 3.”
Quote in the paperSubstantial claim- Rests on an assumption: Exercise can cause rapid glucose drops and hypoglycemia when insulin dosing is not adjusted.
The paper relies on this without support.
Exercise can cause rapid glucose drops and hypoglycemia when insulin dosing is not adjusted.
“Exercise can have profound effects on glucose levels in a person with type 1 diabetes, causing hypoglycemia if dosing is not adjusted.”
“Aerobic exercise can cause rapid drops in glucose levels because of increased glucose uptake during exercise.”
The paper relies on this without support.
Continuous glucagon delivery can cause nausea, so a dual-hormone MPC must avoid it.
“A dual-hormone MPC may deliver insulin continuously; however, it must avoid continuous delivery of glucagon because nausea can occur from too much glucagon.”
The paper relies on this without support.
No earlier group had put exercise directly into the process model of a dual-hormone MPC.
“To the best of our knowledge, no prior groups have incorporated exercise directly into the process model of a DH-MPC.”
The paper relies on this without support.
Individuals can respond differently to exercise.
“whereas different people can respond differently to exercise.”
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?
Previously, the authors showed how an exercise model described by Hernandez-Ordonez et al. [17] could be incorporated into a VPP.
“Previously, we showed how an exercise model described by Hernandez-Ordonez et al. [17] could be incorporated into a VPP [14, 18, 19].”
From A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model (2019) · cites this paper as [18]
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.
A p-value of 0.011 is described as not significant
MajorAt the stated 95% significance level, p = 0.011 is statistically significant, contrary to the claim that the difference did not change significantly.
“While time spent in hypoglycemia with the DH-Pred approach did not change significantly in comparison to the DH-Thr approach (p-value = 0.011)”
“The significance level was 95%.”
Listed state variables total 14, not 13
MinorThe listed groups contain 2 + 4 + 3 + 3 + 2 = 14 state variables, one more than the stated total.
Reported 13 · recomputed 14
“There are 13 state variables, which include two state variables from the glucose kinetics model, four from the insulin kinetics model, three from the insulin dynamic models, three from the glucagon kinetics model and two from the glucagon dynamic model.”
Cites
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MethodModel Predictive Control of Type 1 Diabetes: An <i>in Silico</i> Trial
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“[8], a DH MPC by Boiroux et al.”
MethodResistance Versus Aerobic Exercise
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MethodInfluentialSelf-citationDesign and Evaluation of a Robust PID Controller for a Fully Implantable Artificial Pancreas
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“A number of closed loop algorithms utilize a proportional integral derivative controller [4, 5].”
MethodJacobs, P. G., Youssef, J. E., Castle, J. R., Bakhtiani, P. A., Branigan, D. L., Breen, M. E., Bauer, D., Preiser, N., Leonard, G., Stonex, T. M., et al. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2323248 · Cited by 82 · cited 1× · see citing works
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BackgroundMethodBoiroux, D., Bátora, V., Hagdrup, M., Tárnik, M., Murgaš, J., Schmidt, S., Nørgaard, K., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.10.106 · Cited by 16 · see citing works
Flagged first, then by citations
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Cited by
Sorted by citations
Jacobs, P. G., Herrero, P., Facchinetti, A., Vehi, J., Kovatchev, B., Breton, M. D., Cinar, A., Nikita, K. S., Doyle, F. J., Bondia, J., et al. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3331297 · Cited by 113 · see citing works
Self-citationResalat, N., El Youssef, J., Tyler, N., Castle, J., & Jacobs, P. G. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217301 · Cited by 78 · see citing works
Self-citationMOSQUERA-LOPEZ, C. M., Wilson, L. M., Youssef, J. E., Hilts, W. W., LEITSCHUH, J. A., Branigan, D. L., Gabo, V. B., Eom, J. H., Castle, J. R., & Jacobs, P. G. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00783-1 · Cited by 59 · see citing works
“The OHSU ’ s MPC algorithm 21 uses a glucoregulatory model to predict glucose outcomes over a predicted horizon, and mathematically solves for the optimal insulin dose schedule across the control horizon to bring a person to a target glucose level.”
MethodInfluentialSelf-citationPhysiological Models and Control for Type 1 Diabetes Mellitus: A Brief Review
Nath, A., Biradar, S., Balan, A., Dey, R., & Padhi, R. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.05.077 · Cited by 35 · see citing works
Jacobs, P. G., Resalat, N., Hilts, W. W., Young, G. M., LEITSCHUH, J. A., Pinsonault, J., Youssef, J. E., Branigan, D. L., Gabo, V. B., Eom, J. H., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00112-7 · Cited by 42 · see citing works
“The exMPC algorithm 18–21 (appendix p 2) is a traditional MPC algorithm that uses a linearised version of a compartment model comprising nonlinear differential equations that represent kinetics and dynamics of subcutaneously delivered insulin, carbohydrates, and exercise.”
Self-citationResalat, N., Hilts, W. W., Youssef, J. E., Tyler, N. S., Castle, J. R., & Jacobs, P. G. (2019) · Journal of Diabetes Science and Technology · doi:10.1177/1932296819881467 · Cited by 36 · see citing works
“We used 99 statistically sampled virtual patients from the OHSU virtual patient population to represent patients with T1D (ie, the plant).(9,24) The virtual patient population was generated based on a glucoregulatory model consisting of insulin kinetics and dynamics models and a glucose kinetics model.”
Self-citationImproving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes
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Nguyen, T.-T. P., Jacobs, P. G., Castle, J. R., Wilson, L. M., Kuehl, K. S., Branigan, D. L., Gabo, V. B., Guillot, F. H., Riddell, M. C., Haidar, A., et al. (2020) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00534.2020 · Cited by 30 · see citing works
Self-citationData-Driven Robust Control for Type 1 Diabetes Under Meal and Exercise Uncertainties
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“As in the exercise model of [9, 13, 21, 28], physical activity is represented by parameters MM , the percentage of active muscular mass at time t, and O2 , the percentage of maximum oxygen consumption which can be combined to reproduce arbitrary kinds of physical activity.”
BackgroundMethodInfluentialPaoli, B. D., D’Antoni, F., Merone, M., Pieralice, S., Piemonte, V., & Pozzilli, P. (2021) · Bioengineering · doi:10.3390/bioengineering8060072 · Cited by 23 · see citing works
“In addition, the works addressing this task usually resort to multivariate approaches that ask the patient to manually supply data concerning their state or integrate their data from several heterogeneous sensors [16,17], or consider a dataset composed of virtual patients [18,19].”
BackgroundDiabetes Research and Care Through the Ages
Zinman, B., Skyler, J. S., Riddle, M. C., & Ferrannini, E. (2017) · Diabetes Care · doi:10.2337/dci17-0042 · Cited by 21 · see citing works
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Resalat, N., Youssef, J. E., Reddy, R. K. K., Castle, J. R., & Jacobs, P. G. (2019) · Journal of Process Control · doi:10.1016/j.jprocont.2019.05.018 · Cited by 17 · see citing works
Self-citationData-Driven Robust Control for a Closed-Loop Artificial Pancreas
Paoletti, N., Liu, K. S., Chen, H., Smolka, S. A., & Lin, S. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2912609 · Cited by 16 · see citing works
“[13], which extends Hovorka’s wellestablishedmodel [15] to capture the effect of exercise on BG.”
BackgroundMethodInfluentialDias, C. C., Kamath, S. R., & Vidyasagar, S. (2020) · IET Systems Biology · doi:10.1049/iet-syb.2019.0101 · Cited by 16 · see citing works
“Switching technique such as hysteresis switch was developed and added the flexibility to the control design [51, 52].”
MethodModels, Devices, Properties, and Verification of Artificial Pancreas Systems
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“To create a virtual patient, Paoletti et al used an extended version of the Hovorka ODE model for their “virtual patient” which they coupled with a model predictive control scheme [83, 136].”
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“One potential approach is to use the model presented by Resalat et al. [57] is an extension of the Cambridge model that includes a dynamic physical activity subsystem.”
MethodResearch Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems
Jacobs, P. G., Levy, C. J., Brown, S. A., Riddell, M. C., Çınar, A., Boughton, C. K., Breton, M. D., Dassau, E., Forlenza, G. P., Henderson, R. J., et al. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251338754 · Cited by 15 · see citing works
Self-citationIoT technologies combining glucose control with physiological signal: comparative study
Arbi, K. F., SOULIMANE, O., & Saffih, F. (2020) · doi:10.1109/icee49691.2020.9249843 · Cited by 8 · see citing works
“Peter G. Jacobs and his team [44-45] integrated exercise effect in a dual hormone artificial pancreas using MPC controller, they introduce a mathematical model [46] which describe exercise effect on the glucoregulatory system in the controller.”
MethodYoung, G. M., Jacobs, P. G., Tyler, N. S., Nguyen, T.-T. P., Castle, J. R., Wilson, L. M., Branigan, D. L., Gabo, V. B., Guillot, F. H., Riddell, M. C., et al. (2023) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00298.2022 · Cited by 8 · see citing works
Self-citationRíos, Y. Y., García-Rodríguez, J. A., Sanchez, E. N., et al. (2021) · ISA Transactions · doi:10.1016/j.isatra.2021.07.045 · Cited by 7 · see citing works
The Evolution of Insulin Administration in Type 1 Diabetes
Pinnaro, C. T., & Tansey, M. (2021) · Journal of Diabetes Mellitus · doi:10.4236/jdm.2021.115021 · Cited by 7 · see citing works
Design of dual hormone blood glucose therapy and comparison with single hormone using MPC algorithm
Dias, C. C., Kamath, S. R., & Vidyasagar, S. (2020) · IET Systems Biology · doi:10.1049/iet-syb.2020.0053 · Cited by 5 · see citing works
Event-triggered smart dual hormone artificial pancreas for patient-specific drug delivery
Govindaraju, D., & Subbian, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-18085-9 · Cited by 3 · see citing works
Exercise, Sleep, and Type 1 Diabetes
Jacobs, P. G., & Reddy, R. K. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816658-1.00015-6 · Cited by 2 · see citing works
Self-citationNew Contributions Towards Meal and Exercise Announcement-Free Artificial Pancreas Systems
Sala-Mira, I. (2023) · doi:10.4995/thesis/10251/192684 · Cited by 1 · see citing works
Rapid Model Predictive Controller for Artificial Pancreas
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Estimation of Blood Glucose Concentration During Endurance Sports
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“In connection to the control of BG concentration during PA and after it could also be useful to reduce the risk of hypoglycemia in T1D subjects, and recent work, [2, 7, 8, 12, 13, 16, 18, 19, 21, 24], indicate the importance of handling PA for T1D patients.”
ResultArtificial Intelligence in Automated Hormone Delivery
Jacobs, P. G., & MOSQUERA-LOPEZ, C. M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13244-5.00017-1
Self-citationSafe-Predictive Control of Artificial Pancreas for Hyperglycemia and Hypoglycemia Risk MitigationPreprint
Arab, A., & Mousavi, Y. (2025) · doi:10.36227/techrxiv.174910108.85157723/v1
An AI-enabled dual-hormone model predictive control algorithm that delivers insulin and pramlintide
Jacobs, P. G., Hilts, W. W., Dodier, R. H., LEITSCHUH, J. A., Eom, J. H., Branigan, D. L., Ling, F., Howard, M., MOSQUERA-LOPEZ, C. M., & Wilson, L. M. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.06.011
Self-citationArab, A., Mohamadi, S., Alisafaei, F., Mousavi, Y., & Kurabayashi, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.12.675875
“In [18], a dual-hormone MPC system was introduced to administer insulin and glucose in response to exercise with the sys-tem equations linearized.”
- Authors
- Resalat, N., El Youssef, J., Reddy, R., & Jacobs, P. G.
- Affiliations
- Oregon Health & Science University (US)
- Dept. of Biomedical Engineering, Oregon Health & Science University, Portland, OR, 97239
- Venue
- 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2016-08
- Volume
- 2016
- Pages
- 2270-2273
- ISSN
- 2375-7477 (ISSN-L)
- PMC
- PMC5858942
- OpenAlex
- W2535260790
- Semantic Scholar
- a2fb3410ac5b02cd7a69af573f2288a376ff5fa7
- Cited by
- 34
- References
- 14
- Citations by year
- 2025: 5 · 2024: 1 · 2023: 7 · 2021: 3 · 2020: 7 · 2019: 8 · 2018: 1 · 2017: 2
- FWCI
- 30.89
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (DP3DK101044)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
- Keywords
- artificial pancreas, type 1 diabetes, exercise model, hypoglycemia prevention, glycemic control, switching control
- MeSH terms
- Blood Glucose, Diabetes Mellitus, Type 1, Glucagon, Humans, Hypoglycemia, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Models, Biological, Pancreas, Artificial
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
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 ›
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