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Record W3134137658 · doi:10.2337/dc20-1674

Glycemic Outcome Associated With Insulin Pump and Glucose Sensor Use in Children and Adolescents With Type 1 Diabetes. Data From the International Pediatric Registry SWEET

2021· article· en· W3134137658 on OpenAlexaff
Roque Cardona‐Hernandez, Anke Schwandt, Hessa Alkandari, Heiko Bratke, Agata Chobot, Nicole Coles, Sarah Corathers, Damla Gökşen, Peter W Goss, Zineb Imane, Katrin Nagl, Stephen O’Riordan, Craig Jefferies

Bibliographic record

VenueDiabetes Care · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsMarkham Stouffville Hospital
Fundersnot available
KeywordsMedicineInsulin pumpType 1 diabetesDiabetic ketoacidosisDiabetes mellitusHypoglycemiaGlycemicLogistic regressionCohortType 2 diabetesInternal medicineInsulinPediatricsEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVE Insulin delivery methods, glucose-monitoring modalities, and related outcomes were examined in a large, international, diverse cohort of children and adolescents with type 1 diabetes from the Better Control in Pediatric and Adolescent Diabetes: Working to Create Centers of Reference (SWEET) -Registry. RESEARCH DESIGN AND METHODS Participants with type 1 diabetes of ≥1 year, aged ≤18 years, and who had documented pump or sensor usage during the period August 2017–July 2019 were stratified into four categories: injections–no sensor (referent); injections + sensor; pump–no sensor; and pump + sensor. HbA1c and proportion of patients with diabetic ketoacidosis (DKA) or severe hypoglycemia (SH) were analyzed; linear and logistic regression models adjusted for demographics, region, and gross domestic product per capita were applied. RESULTS Data of 25,654 participants were analyzed. The proportions of participants (adjusted HbA1c data) by study group were as follows: injections–no sensor group, 37.44% (8.72; 95% CI 8.68–8.75); injections + sensor group, 14.98% (8.30; 95% CI 8.25–8.35); pump–no sensor group, 17.22% (8.07; 95% CI 8.03–8.12); and pump + sensor group, 30.35% (7.81; 95% CI 7.77–7.84). HbA1c was lower in all categories of participants who used a pump and/or sensor compared with the injections–no sensor treatment method (P < 0.001). The proportion of DKA episodes was lower in participants in the pump + sensor (1.98%; 95% CI 1.64–2.48; P < 0.001) and the pump–no sensor (2.02%; 95% CI 1.64–2.48; P < 0.05) groups when compared with those in the injections–no sensor group (2.91%; 95% CI 2.59–3.31). The proportion of participants experiencing SH was lower in pump–no sensor group (1.10%; 95% CI 0.85–1.43; P < 0.001) but higher in the injections + sensor group (4.25%; 95% CI 3.65–4.95; P < 0.001) compared with the injections–no sensor group (2.35%; 95% CI 2.04–2.71). CONCLUSIONS Lower HbA1c and fewer DKA episodes were observed in participants using either a pump or continuous glucose monitoring (CGM) or both. Pump use was associated with a lower rate of SH. Across SWEET centers, use of pumps and CGM is increasing. The concomitant use of pump and CGM was associated with an additive benefit.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.263
Teacher spread0.238 · 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

Citations124
Published2021
Admission routes1
Has abstractyes

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