MétaCan
Menu
Back to cohort
Record W4384924871 · doi:10.2337/dci23-0021

The Growing Role of Technology in the Care of Older Adults With Diabetes

2023· review· en· W4384924871 on OpenAlexaff
Elbert S. Huang, Alan J. Sinclair, Paul R. Conlin, Tali Cukierman‐Yaffe, Irl B. Hirsch, Megan Huisingh‐Scheetz, Anna R. Kahkoska, Lori M. Laffel, Alexandra K. Lee, Sei Lee, Kasia J. Lipska, Graydon S. Meneilly, Naushira Pandya, Monica E. Peek, Anne L. Peters, Richard E. Pratley, Diana Sherifali, Elena Toschi, Guillermo E. Umpierrez, Ruth S. Weinstock, Medha Munshi

Bibliographic record

VenueDiabetes Care · 2023
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsMcMaster UniversityUniversity of British Columbia
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on AgingAbbott Diabetes CareNational Academy of MedicineNational Institutes of HealthNovo NordiskDexcomInsulet CorporationUniversity of ChicagoSanofiDiabetes Research ConnectionBayerPfizerEli Lilly and CompanyLeona M. and Harry B. Helmsley Charitable TrustU.S. Department of Veterans Affairs
KeywordsMedicineDiabetes mellitusGerontologyHealth careEmpowermentDiabetes managementBiomedical technologyPatient EmpowermentEmerging technologiesPopulationMEDLINEType 2 diabetesEnvironmental healthEconomic growth

Abstract

fetched live from OpenAlex

The integration of technologies such as continuous glucose monitors, insulin pumps, and smart pens into diabetes management has the potential to support the transformation of health care services that provide a higher quality of diabetes care, lower costs and administrative burdens, and greater empowerment for people with diabetes and their caregivers. Among people with diabetes, older adults are a distinct subpopulation in terms of their clinical heterogeneity, care priorities, and technology integration. The scientific evidence and clinical experience with these technologies among older adults are growing but are still modest. In this review, we describe the current knowledge regarding the impact of technology in older adults with diabetes, identify major barriers to the use of existing and emerging technologies, describe areas of care that could be optimized by technology, and identify areas for future research to fulfill the potential promise of evidence-based technology integrated into care for this important population.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.900
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.293
Teacher spread0.279 · 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 designOther design
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

Citations65
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDiabetes CareSame topicDiabetes Management and ResearchFrench-language works237,207