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Automated Insulin Delivery Systems and Glucose Management in Children and Adolescents With Type 1 Diabetes

2025· article· en· W4414058852 on OpenAlexaff
Hannah Steiman De Visser, Seerat Waraich, Manik Chhabra, Jennifer M. Yamamoto, Ian Zenlea, Nicole Askin, Rasheda Rabbani, Jonathan McGavock, Dana Greenberg, Marley Greenberg, ETHAN PARIKH, Cameron Keighron, Laura Nemi

Bibliographic record

VenueJAMA Pediatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsGeorge & Fay Yee Centre for Healthcare InnovationDiabetes CanadaTrillium Health CentreUniversity of ManitobaHealth Sciences CentreChildren's Hospital Research Institute of ManitobaQueen's UniversityManitoba Health
Fundersnot available
KeywordsType 1 diabetesInsulin deliveryDiabetes mellitusInsulinAdverse effectContinuous glucose monitoringMEDLINEBlood Glucose Self-Monitoring

Abstract

fetched live from OpenAlex

Importance: Youth living with type 1 diabetes (T1D) are increasingly choosing automated insulin delivery (AID) systems to manage their blood glucose. Few systematic reviews meta-analyzing results from randomized clinical trials (RCTs) are available to guide decision-making. Objective: To study the association of prolonged AID system use in an outpatient setting with measures of glucose management and quality of life in youth with T1D. Data Sources: MEDLINE, Embase, CINAHL, and Cochrane Central were searched from January 2017 to March 2025 to identify eligible RCTs. Study Selection: Two reviewers independently performed literature screening, data extraction, and quality assessment. Included in the analysis were RCTs of youth aged 6 to 18 years with T1D that assessed the efficacy of AID systems in outpatient settings longer than 48 hours compared with any other insulin regimen. Data Extraction and Synthesis: Two reviewers performed data extraction and quality assessment independently and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and PRISMA literature search extension guidelines. Random-effects meta-analysis models were used to estimate the pooled measures of efficacy as a mean difference (MD) with 95% CIs for outcomes measures. Main Outcomes and Measures: The 2 primary outcome measures were time in range (TIR) and glycated hemoglobin (HbA1c). Results: Of 2363 citations retrieved, 11 RCTs (n = 901 participants) with measures of HbA1c and 10 RCTs (n = 786 participants) with measures of TIR were included. RCTs tested interventions lasting a mean (SD) of 31 (26) weeks on youth with a median age of 12 years (range, 10.8-15.9 years); 51% were female, mean (SD) HbA1c level was 8.4% (1.1%), and mean (SD) TIR was 51% (9%). Random-effects models revealed that, compared with any insulin regimen, HbA1c level was reduced -0.41% (95% CI, -0.58% to -0.25%; I2 = 39%), whereas TIR increased 11.5% (95% CI, 9.3%-13.7%; I2 = 23%) with nighttime TIR increasing 19.7% (95% CI, 17.0%-22.4%; I2 = 36%). Random-effects models also revealed that AID use was associated with reduced time spent in hypoglycemia (<3.9 mml/L; MD = -0.32%; 95% CI, -0.60% to -0.03%; I2 = 18%) and hyperglycemia (>10 mmol/L; MD = -10.8%; 95% CI, -14.4% to -7.2%; I2 = 55%), particularly during the night (MD = -14.4%; 95% CI, -19.9% to -8.9%; I2 = 79%) compared with any insulin regimen. There were no differences in adverse events between study arms. Only 2 studies reported changes in QOL. Conclusions and Relevance: This systematic review and meta-analysis found that compared with any other insulin regimen, use of AID systems by youth with T1D was associated with clinically meaningful improvements in multiple measures of glucose management, including the risk of both hyperglycemia and hypoglycemia, without increasing the risk of adverse events. More data are needed on the efficacy of AID systems on patient report outcomes.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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 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

Citations12
Published2025
Admission routes1
Has abstractyes

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