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Record W2130590674 · doi:10.1016/j.aprim.2013.12.004

Diabetes in older people: Prevalence, incidence and its association with medium- and long-term mortality from all causes

2014· article· en· W2130590674 on OpenAlexaff
Mercedes Sánchez Martínez, Augusto Blanco, María Victoria Castell, Alicia Gutiérrez-Misis, Juan Ignacio González-Montalvo, Marı́a Victoria Zunzunegui, À. Otero

Bibliographic record

VenueAtención Primaria · 2014
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineDiabetes mellitusIncidence (geometry)DemographyCohortComorbidityPopulationProportional hazards modelCohort studyFamily historyInternal medicineGerontologyEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

To estimate the prevalence and incidence of self-reported diabetes and to study its association with medium- and long-term mortality from all causes in persons ≥65 years. A population-based cohort study begun in 1993. “Envejecer en Leganés” cohort (Madrid). A random sample of persons ≥65 years (n = 1277 in the 1993 baseline sample). Participants were classified as having diabetes if they so reported and had consulted a physician for this reason within the last year. Diabetes history was categorized in <10 and ≥10 years in 1993. Incidence density was calculated in 2-year periods in non-diabetic individuals (1965 persons/2 years). Vital status was recorded on 31 December 2011. The association between diabetes history ≥10 years and mortality at 6 and 18 years follow-up was studied by the Kaplan–Meier and Cox regression analyses after adjusting for age, sex, heart disease and comorbidity. The prevalence of self-reported diabetes rose from 10.3% in 1993 to 16.1% in 1999 (p ≤ 0.001) and was higher in women than men (p ≤ 0.05). Total incidence density was 2.6 cases/100 persons/2 years (95% CI: 2.0–3.3). Medium- and long-term mortality was higher in persons with diabetes history ≥10 years than in non-diabetic individuals (HR: 2.0; 95% CI: 1.2–3.3 and HR: 1.7; 95% CI: 1.1–2.5, respectively). In diabetics with history <10 years the HR was 1.3 (95% CI: 0.9–1.9) and HR: 1.5 (95% CI: 1.2–1.9, respectively). Although diabetes is clearly associated with increased risk of mortality, it is significant only for patients with ≥10 years’ history of diabetes. Calcular la prevalencia y la incidencia de diabetes autorreferida y analizar su asociación con la mortalidad general a medio y a largo plazo en personas ≥ 65 años. Estudio de cohortes de base poblacional iniciado en 1993. Cohorte «Envejecer en Leganés» (Madrid). Muestra aleatoria de los ≥ 65 años (n = 1.277 en 1993). Diabético: autorreferido y haber visitado al médico por este motivo el último año. Antigüedad de diabetes: más y menos de 10 años en 1993. Prevalencia en 1993, 1995, 1997 y 1999. Densidad de incidencia calculada para periodos bianuales (1.965 personas/2 años). Estado vital registrado a 31 de diciembre de 2011. La asociación entre diabetes y mortalidad a 6 y 18 años se estudió mediante Kaplan-Meier y regresión de Cox, ajustando por edad, sexo, enfermedades del corazón y comorbilidad. La prevalencia de diabetes autorreferida aumentó desde el 10,3% (1993) hasta el 16,1% (1999) (p ≤ 0,001), siendo superior en mujeres (p ≤ 0,05). La incidencia de diabetes fue de 2,66 casos por 100 personas/2 años (IC 95%: 1,9-3,3). La mortalidad a medio y a largo plazo fue mayor en diabéticos con ≥ 10 años que en no diabéticos (HR: 2,0; IC 95%: 1,2-3,3, y HR: 1,7; IC 95%: 1,1-2,5, respectivamente). En diabéticos < 10 años el HR para mortalidad fue 1,3 (IC 95%: 0,9-1,9) y 1,5 (IC 95%: 1,2-1,9), respectivamente. Aunque padecer diabetes se asocia a un aumento de la mortalidad, esta asociación es significativa solo para los pacientes con historia de diabetes igual o superior a 10 años.

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)0.001
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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".

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Citations22
Published2014
Admission routes1
Has abstractyes

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