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Record W4411977895 · doi:10.1089/dia.2025.18802.hz

Early Stages of Automated Insulin Delivery

2025· review· en· W4411977895 on OpenAlexaff
Howard Zisser, Roman Hovorka, Ananda Basu, Tadej Battelino, Charlotte K. Boughton, Marc D. Breton, Bruce A. Buckingham, Sue A. Brown, Daniel R. Cherñavvsky, Eyal Dassau, Mark D. DeBoer, Francis J. Doyle, Laya Ekhlaspour, Chiara Fabris, Gregory P. Forlenza, Ahmad Haidar, David C. Klonoff, Boris Kovatchev, Aaron J. Kowalski, Carol J. Levy, Yogish C. Kudva, David M. Maahs, Moshe Phillip, Éric Renard, Steven J. Russell, Viral N. Shah, Garry M. Steil, R. Paul Wadwa, Stuart A. Weinzimer

Bibliographic record

VenueDiabetes Technology & Therapeutics · 2025
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsMcGill University
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineInsulin deliveryDiabetes mellitusInsulinIntensive care medicineInternal medicineType 1 diabetesEndocrinology

Abstract

fetched live from OpenAlex

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.002

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 head0.036
GPT teacher head0.355
Teacher spread0.319 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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