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Record W2023700590 · doi:10.12927/hcpol.2010.21901

Age Difference Explains Gender Difference in Cardiac Intervention Rates After Acute Myocardial Infarction

2010· article· en· W2023700590 on OpenAlexaffvenue
Randy Fransoo, Patricia J. Martens, Heather J. Prior, Elaine Burland, Dan Château, Alan Katz

Bibliographic record

VenueHealthcare policy · 2010
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsManitoba Health
Fundersnot available
KeywordsMedicineMyocardial infarctionCohortSex characteristicsDemographySignificant differenceGender disparityInternal medicineAge groupsPopulationCardiology

Abstract

fetched live from OpenAlex

Many investigators have reported higher rates of cardiac procedures for males than females after acute myocardial infarction (AMI), suggesting that men are treated more aggressively than women. However, others have reported no significant differences after controlling for age, resulting in uncertainty about the existence of a true gender bias in cardiac care. In this study, a population-based cohort approach was used to calculate age-specific procedure rates by sex from administrative data. Chi-square tests and generalized linear modelling were used to assess gender differences and interactions. For all four procedures studied, rates were significantly higher for males than females (p<0.01). However, age-specific rates revealed few significant differences by gender and a sharp decrease in intervention rates with age for both males and females. Generalized linear modelling confirmed that patient age was a significant predictor of intervention rates, whereas sex was not. The significant gender difference in overall rates was completely confounded by the older age profile of female AMI patients compared to their male counterparts.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.042
GPT teacher head0.390
Teacher spread0.348 · 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.

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

Citations10
Published2010
Admission routes2
Has abstractyes

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