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Record W3179739515 · doi:10.1161/jaha.120.019991

Diabetes Mellitus Is Associated With Poor In‐Hospital and Long‐Term Outcomes in Young and Midlife Stroke Survivors

2021· article· en· W3179739515 on OpenAlexafffundabout
Bradley J. MacIntosh, Ellen Cohen, Jessica Colby‐Milley, Jiming Fang, Limei Zhou, Michael Ouk, Che‐Yuan Wu, Baiju R. Shah, Krista L. Lanctôt, Nathan Herrmann, Elizabeth Linkewich, Marcus Law, Sandra E. Black, Richard H. Swartz, Moira K. Kapral, Jodi D. Edwards, Walter Swardfager

Bibliographic record

VenueJournal of the American Heart Association · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsHealth Sciences CentreUniversity of TorontoToronto Rehabilitation InstituteSunnybrook Health Science CentreHeart and Stroke FoundationUniversity of OttawaOntario Brain Institute
FundersOntario Ministry of Health and Long-Term CareCanadian Institutes of Health ResearchToronto Rehabilitation InstituteSunnybrook Research InstituteAlzheimer's AssociationUniversity of OttawaFondation Brain CanadaInstitute for Clinical Evaluative SciencesHeart and Stroke Foundation of Canada
KeywordsMedicineStroke (engine)Diabetes mellitusProportional hazards modelHazard ratioOdds ratioInternal medicineIncidence (geometry)Logistic regressionDementiaPediatricsEmergency medicineConfidence intervalDisease

Abstract

fetched live from OpenAlex

Background The incidence of ischemic stroke has increased among adults aged 18 to 64 years, yet little is known about relationships between specific risk factors and outcomes. This study investigates in-hospital and long-term outcomes in patients with stroke aged <65 years with preexisting diabetes mellitus. Methods and Results Consecutive patients aged <65 years admitted to comprehensive stroke centers for acute ischemic stroke between 2003 and 2013 were identified from the Ontario Stroke Registry. Multinomial logistic regression was used to estimate adjusted odds ratio (OR [95% CI]) of in-hospital mortality or direct discharge to long-term or continuing care. Cox proportional hazards regression was used to estimate the adjusted hazards ratio (aHR [95% CI]) of long-term mortality, readmission for stroke/transient ischemic attack, admission to long-term care, and incident dementia. Predefined sensitivity analyses examined stroke outcomes among young (aged 18-49 years) and midlife (aged 50-65 years) subgroups. Among 8293 stroke survivors (mean age, 53.6±8.9 years), preexisting diabetes mellitus was associated with a higher likelihood of in-hospital death (adjusted OR, 1.46 [95% CI, 1.14-1.87]) or direct discharge to long-term care (adjusted OR, 1.65 [95% CI, 1.07-2.54]). Among stroke survivors discharged (N=7847) and followed up over a median of 6.3 years, preexisting diabetes mellitus was associated with increased hazards of death (aHR, 1.68 [95% CI, 1.50-1.88]), admission to long-term care (aHR, 1.57 [95% CI, 1.35-1.82]), readmission for stroke/transient ischemic attack (aHR, 1.37 [95% CI, 0.21-1.54]), and incident dementia (aHR, 1.44 [95% CI, 1.17-1.77]). Only incident dementia was not increased for young stroke survivors. Conclusions Focused secondary prevention and risk factor management may be needed to address poor long-term outcomes for patients with stroke aged <65 years with preexisting diabetes mellitus.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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

Citations16
Published2021
Admission routes3
Has abstractyes

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