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Record W4409068303 · doi:10.1016/j.hctj.2025.100101

Quality of life in young adults with type 1 diabetes

2025· article· en· W4409068303 on OpenAlexaff
Marissa N. Baudino, Samantha A Carreon, Randi Streisand, Tricia S. Tang, Sarah K. Lyons, Siripoom McKay, Barbara J. Anderson, Charles G. Minard, Sridevi Devaraj, Ashley M. Butler, Marisa E. Hilliard

Bibliographic record

VenueHealth Care Transitions · 2025
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesJuvenile Diabetes Research Foundation United States of America
KeywordsType 2 diabetesQuality of life (healthcare)Quality (philosophy)GerontologyType 1 diabetesPsychologyMedicineDiabetes mellitusEndocrinologyPsychotherapistPhysics

Abstract

fetched live from OpenAlex

Introduction: Challenges of young adulthood with type 1 diabetes (T1D) include transitioning to adult care, increased T1D self-management responsibilities, and normal developmental transitions. Recognizing patterns of health-related quality of life (HRQOL) across a demographically and clinically broad range of young adults with T1D may help identify who needs additional support as they transfer to adult healthcare. We hypothesized that young adults from specific demographic and clinical groups would report lower HRQOL. Methods: =8.8 ± 2.0 %) self-reported demographics and HRQOL; A1c was analyzed via point of care or dried blood spot. ANOVAs and t-tests were used to compare HRQOL by demographic (gender, race/ethnicity, insurance, school enrollment) and clinical variables (device use, A1c). Results: Diabetes-specific HRQOL differed significantly by gender and school enrollment; females and young adults enrolled in school reported higher HRQOL. There were no significant differences in HRQOL across race/ethnicity, insurance type, and diabetes technology use. Conclusion: Monitoring HRQOL may be helpful to identify diabetes-specific psychosocial needs during the transition from pediatric to adult healthcare. Patterns suggest males and those not in school may benefit from additional support.

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)0.001
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 score0.054
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.428
Teacher spread0.380 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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