Evidence map›Paper›PMID 40899768›Full record

ArticleJournal of diabetes science and technology2026

Automated Oral Minimal Models for Rapid Estimation of Insulin Sensitivity and Beta-Cell Responsivity in Large-Scale Data Sets: A Validation Study.

Simone Perazzolo, Alfonso Galderisi, Alice Carr, Colin Dayan, Claudio Cobelli

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes science and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Simone PerazzoloNanomath LLC, Spokane, WA, USA.ORCID 0000-0003-4498-7823
Alfonso GalderisiYale University, New Haven, CT, USA.
Alice CarrUniversity of Alberta, Edmonton, AB, Canada.ORCID 0000-0003-0704-8843
Colin DayanUniversity of Cardiff, Cardiff, UK.
Claudio CobelliUniversity of Padova, Padova, Italy.ORCID 0000-0002-0169-6682

Funding

Yale Diabetes Research CenterP30DK045735 · NIDDK · YALE UNIVERSITY · PI GERALD I SHULMAN · 1993 to 2026
$44.0M
NIDDK NIH HHS P30 DK045735
6 · The paper itself

Abstract

The Oral Minimal Model (OMM) analysis offers unique measures of glucose-insulin regulation during glucose challenges. However, its manual test-by-test implementation limits scalability in large studies. We introduce the Automated Oral Minimal Model (AOMM), a tool that streamlines and automates the entire OMM workflow while preserving analytical fidelity, enabling efficient batch processing of large datasets. Built on SAAM II software, AOMM was validated against manually extracted results from

Indexed as

Blood GlucoseInsulinInsulin ResistanceInsulin-Secreting CellsModels, BiologicalAlgorithmsHumansReproducibility of ResultsSoftwareBlood GlucoseInsulinbeta-cell responsivitydiabetes algorithmsglucose minimal modelinsulin sensitivityoral minimal modelSAAM II

Identifiers

PMID40899768
PMCPMC12408534

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.