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Record W4381337141 · doi:10.2337/db23-1454-p

1454-P: Food Insecurity in People with Type 1 Diabetes and Glycemic Outcomes

2023· article· en· W4381337141 on OpenAlexaboutno aff
EMMA L. OSPELT, Nudrat Noor, JEEHEA SONYA HAW, SUSAN HSIEH, Ruth S. Weinstock, David W. Hansen, Kristina Cossen, Kathryn L. Fantasia, Ines Guttmann‐Bauman, Vandana Raman, Berhane Seyoum, Osagie Ebekozien

Bibliographic record

VenueDiabetes · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEnvironmental healthType 2 diabetesDemographyPopulationGerontologyOdds ratioCohortOddsDiabetes mellitusGlycemicPsychological interventionLogistic regressionInternal medicineEndocrinologyPsychiatry

Abstract

fetched live from OpenAlex

Background: This multi-center study aims to explore relationships between individuals with type 1 diabetes (T1D) who are at risk for food insecurity and glycemic outcomes. Methods: Electronic health record data from T1D Exchange Quality Improvement Collaborative (T1DX-QI) categorized completed Hunger Vital Signs (HVS) questionnaires into at risk and no risk for food insecurity. At-risk individuals were defined as selecting “often true” or “sometimes true” to screening questions, whereas primary outcome was recent A1c levels. Demographic information and diabetes related covariates were assessed for potential relationships. Results: In the total T1D cohort (N=4453), 3.8% were at risk for food insecurity. Stratifying by age, 6% of youth (<18 years) and 5% of adults (>18 years) were at risk. Statistically significant differences were observed among food insecurity risk and insurance type. Of those at risk, 54% had public insurance compared with 7% private. Individuals at risk had a higher mean HbA1c when compared with individuals not at risk (9.4% vs 8.5% respectively, p<.001). The odds of an individual having an HbA1c >7% were higher in the group at risk relative to the group not at risk OR1.4 (95% CI 0.9, 2.1). Conclusion: Individuals in this population who were at risk for food insecurity also had elevated A1c levels. Interventions are needed to identify effective ways of improving food insecurity among people with T1D. Disclosure E.L.Ospelt: None. V.Raman: None. B.Seyoum: None. O.Ebekozien: Advisory Panel; Medtronic, Research Support; Eli Lilly and Company, Dexcom, Inc. N.Noor: None. J.Haw: None. S.Hsieh: None. R.S.Weinstock: Consultant; Jaeb Center for Health Research, Other Relationship; Wolters Kluwer Health, Research Support; Insulet Corporation, Medtronic, Eli Lilly and Company, Novo Nordisk, Boehringer Ingelheim Inc., Hemsley Charitable Trust, National Institute of Diabetes and Digestive and Kidney Diseases, Tandem Diabetes Care, Inc., Kowa Pharmaceuticals America, Inc. D.W.Hansen: Research Support; 3Boehringer Ingelheim Canada Ltd./Ltée, Lilly, Insulet Corporation, T1D Exchange, Medtronic. K.Cossen: None. K.Fantasia: None. I.Guttmann-bauman: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

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 score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.091
GPT teacher head0.390
Teacher spread0.300 · 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
Published2023
Admission routes1
Has abstractyes

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