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Record W1970076230 · doi:10.2337/diacare.27.2.407

Health-Related Quality of Life and Health-Adjusted Life Expectancy of People With Diabetes in Ontario, Canada, 1996–1997

2004· article· en· W1970076230 on OpenAlexaffabout
Douglas G. Manuel, Susan Schultz

Bibliographic record

VenueDiabetes Care · 2004
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of Toronto
Fundersnot available
KeywordsLife expectancyMedicineDiabetes mellitusGerontologyQuality of life (healthcare)PopulationPublic healthDemographyEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVE: To estimate the burden of illness from diabetes using a population health survey linked to a population-based diabetes registry. RESEARCH DESIGN AND METHODS: Measures of health-related quality of life (HRQOL) from the 1996/97 Ontario Health Survey (n = 35,517) were combined with diabetes prevalence and mortality data from the Ontario Diabetes Database (n = 487,576) to estimate the impact of diabetes on life expectancy, health-adjusted life expectancy (HALE), and HRQOL. RESULTS: Life expectancy of people with diabetes was 64.7 and 70.7 years for men and women, respectively-12.8 and 12.2 years less than that for men and women without diabetes. Diabetes had a large impact on instrumental and basic activities of daily living, more so than on functional health. HALE was 58.3 and 62.7 years, respectively, for men and women-11.9 and 10.7 years less than that of men and women without diabetes. Eliminating diabetes would increase Ontario life expectancy by 2.8 years for men and 2.6 years for women; HALE would increase by 2.7 and 3.2 years for men and women, respectively. CONCLUSIONS: The burden of illness from diabetes in Ontario is considerable. Efforts to reduce diabetes would likely result in a "compression of morbidity." An approach of estimating diabetes burden using linked data sources provides a robust approach for the surveillance of diabetes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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

Citations149
Published2004
Admission routes2
Has abstractyes

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