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Record W2184214573

Myocardial infarction in Québec rural and urban populations between 1995 and 1997.

2007· article· en· W2184214573 on OpenAlexaffabout
Julie Loslier, Alain Vanasse, Theo Niyonsenga, Josiane Courteau, Gabriela Orzanco, Abbas Hemiari

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsRuralityCensusSocioeconomic statusDemographyRural areaMedicineGeographyIncidence (geometry)Myocardial infarctionPopulationEnvironmental healthCardiology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: There is abundant evidence of health inequities between urban and rural populations. The purpose of this paper is to describe the socioeconomic characteristics of Québec urban and rural populations and the relation between rurality and incidence of myocardial infarction (MI), care management and outcomes. METHODS: Socioeconomic data by census subdivisions were available from the 1996 Canadian census, representing 7,137,245 individuals. Data on patients with MI were taken from the provincial administrative health database (MED-ECHO), which is managed by the Ministry of Health and contains clinical and demographic information collected when patients are released from acute care hospitals in Québec. RESULTS: We included a total of 37,678 cases compiled over the 3 years of follow-up in the analyses. Residents of rural areas with low urban influence have higher MI incidence rates than all of the other populations in the study. In comparison with urban populations, their observed rural counterparts are at a disadvantage with regard to education, employment and income. Although angioplasty and coronary artery bypass graft surgery rates were higher in more urban areas, the survival rate was lower than in rural areas. CONCLUSION: This study revealed geographic heterogeneity of MI incidence, revascularization rates and survival rates among urban and rural populations.

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 score0.073
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.056
GPT teacher head0.378
Teacher spread0.321 · 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

Citations14
Published2007
Admission routes2
Has abstractyes

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