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Record W2393700869

The effects of declining β-cell mass on the development of diabetes

2008· article· en· W2393700869 on OpenAlexaff
Qinghua Wang

Bibliographic record

VenueInternational Journal of Internal Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsNeogenesisDiabetes mellitusInternal medicineEndocrinologyType 2 diabetesInsulinGlycemicGlucose homeostasisMedicineIsletInsulin resistanceGlucagonCellBiologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Hyperglycemia is the characteristic sign of diabetes,and a functional pancreatic islet β cell mass is an important factor in regulating glucose homeostasis.The majority of patients with insulin resistance as a result of obesity do not develop diabetes because their capacity for β cell compensation is maintained.As long as the cells can keep pace with demand,diabetes can be avoided.Recent studies suggested that β cell mass deterioration played an important role in the onset of diabetes.A lot of experiments related to prediabetic models indicated that the development of diabetes could be prevented or delayed as a result of enhancing β cell mass,increasing β cell neogenesis and proliferation.In type 2 diabetic patients,glucagon like peptide-1(GLP-1) could restore the β-cell sensitivity to glucose and dominantly improved the glycemic control.GLP-1 also enhanced glucose-induced insulin secretion and insulin-mediated glucose-disposal.It can be predicted enhancing functional β cell mass will be useful in prevention and treatment of human type 2 diabetes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.281
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2008
Admission routes1
Has abstractyes

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