MétaCan
Menu
Back to cohort
Record W2073952198 · doi:10.1192/bjp.bp.109.067082

Treatment of ischaemic heart disease and stroke in individuals with psychosis under universal healthcare

2009· article· en· W2073952198 on OpenAlexafffund
Leslie Anne Campbell, Yan Wang

Bibliographic record

VenueThe British Journal of Psychiatry · 2009
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCapital District Health AuthorityDalhousie University
FundersDalhousie UniversityHeart and Stroke Foundation of Canada
KeywordsStroke (engine)MedicinePsychosisOdds ratioPopulationSchizophrenia (object-oriented programming)Health careCoronary artery diseaseGuidelineDiseaseHeart diseaseAtrial fibrillationInternal medicinePsychiatryEmergency medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Most data on the quality of vascular care for individuals with psychiatric conditions come from countries without universal healthcare. AIMS: To investigate the treatment of people with psychosis admitted for ischaemic heart disease or stroke under universal healthcare. METHOD: A population-based study of administrative data comparing Canadians with and without a history of schizophrenia or related psychosis (n = 65,039). RESULTS: Of 49 248 admissions for ischaemic heart disease, 1285 had a history of psychosis. Despite a higher 1-year mortality, they were less likely to receive guideline-consistent treatment: e.g. coronary artery bypass grafting (adjusted odds ratio (OR) = 0.35, 95% CI 0.25-0.48), beta-blockers (adjusted OR = 0.82, 95% CI 0.71-0.95) and statins (adjusted OR = 0.51, 95% CI 0.41-0.63). Of 15 791 admissions for stroke, 594 had a history of psychosis. Despite higher 1-year mortality rates, they were less likely to receive cerebrovascular arteriography or warfarin. CONCLUSIONS: People with a history of psychosis do not receive equitable levels of vascular care under universal healthcare.

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 score0.078
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.295
Teacher spread0.280 · 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

Citations117
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueThe British Journal of PsychiatrySame topicSchizophrenia research and treatmentFrench-language works237,207