MétaCan
Menu
Back to cohort
Record W4324290796 · doi:10.56827/jrsmms.2022.1001.13

A MATHEMATICAL MODEL OF GLUCOSE HOMEOSTASIS IN CHAD CONTEXT

2022· article· en· W4324290796 on OpenAlexaff
Adam Hassan Adoum, Mahamat Saleh Daoussa . Haggar, Temga . Djaokamla, Jean Marie Ntaganda

Bibliographic record

VenueJOURNAL OF RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsToronto Metropolitan University
FundersUniversity of Rwanda
KeywordsContext (archaeology)Glucose homeostasisMathematical modelComputer scienceNonlinear systemInverse problemInsulinApplied mathematicsBiological systemMathematicsBiologyInsulin resistancePhysicsBiotechnology

Abstract

fetched live from OpenAlex

During these decades, mathematical modeling has become a key domain in science, especially in biomedical sciences. It allows for an experimental and rigorous approach. Thanks to mathematical modeling, the glucose-insulin system could be materialized, which is also theoretical, in order to analyze and interpret it and to predict the results. Many of the mathematical models of the glucose-insulin system have emerged in recent years. In literature, there are models that show the role of physical activity and response of mathematical model to glucose-insulin system dynamics. We propose the mathematical model of ordinary differential equations to investigate simple homeostasis generated by the dynamics of physiological parameters of the glucose-insulin system during physical activity for a healthy subject. Model parameters are estimated using a nonlinear optimization method generally based on inverse problems. The numerical simulations show that the proposed model is adaptable to the data collected in Chad and can be used to test glucose homeostasis for glucose-insulin system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.323
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJOURNAL OF RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCESSame topicDiabetes Management and ResearchFrench-language works237,207