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Record W47486716

Outcomes of acute myocardial infarction in Canada.

2003· article· en· W47486716 on OpenAlexaffabout
Jack V. Tu, Peter C. Austin, Woganee A Filate, Susan Brien, Louise Pilote, David A. Alter

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMyocardial infarctionAnginaMortality rateHeart failureUnstable anginaHospital dischargeEmergency medicinePopulationInternal medicineDatabaseEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Little information is available on recent population-based trends in the outcomes of patients who have had an acute myocardial infarction (AMI) in Canada. METHODS: Data were analyzed from the Discharge Abstract Database and Hospital Morbidity Database of the Canadian Institute for Health Information. All new cases of AMI in Canada between fiscal 1997/98 and fiscal 1999/2000 of patients at least 20 years old were examined. Data were also analyzed from these databases for hospital readmissions for a second AMI, angina and congestive heart failure (CHF). RESULTS: There were 139,523 new AMI cases. The overall crude in-hospital AMI mortality rate in Canada was 12.3%. In-hospital mortality rate after an AMI was worse for women than for men in Canada (16.7% and 9.9%, respectively). The age- and sex-standardized in-hospital mortality rate varied from a low of 10.5% (95% CI 8.4% to 12.6%) in Prince Edward Island to a high of 13.1% (95% CI 12.8% to 13.5%) in Quebec. Among AMI survivors, 12.5% were readmitted within one year for angina, 7.7% for a second AMI and 7.5% for CHF. There were wide interregional differences in age- and sex-standardized mortality rates and one-year readmission rates. CONCLUSIONS: AMI is associated with a substantial acute mortality rate in Canada, especially in the elderly and female patients. Identifying the causes of interregional differences in patient outcomes should be a priority for future research.

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 score0.292
Threshold uncertainty score0.915

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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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

Citations54
Published2003
Admission routes2
Has abstractyes

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