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Record W4406756747 · doi:10.1016/j.cjco.2025.01.016

Equity Gaps in the Diagnosis and Treatment of Occlusion Myocardial Infarction

2025· article· en· W4406756747 on OpenAlexafffund
Varunaavee Sivashanmugathas, Mazen El-Baba, Marcella K. Jones, Alexander Kiss, H. Pendell Meyers, Stephen W. Smith, Lucas B. Chartier, Jesse McLaren

Bibliographic record

VenueCJC Open · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
FundersUniversity Health NetworkBaxter International
KeywordsCardiologyOcclusionMyocardial infarctionEquity (law)MedicineInternal medicineInfarctionPolitical science

Abstract

fetched live from OpenAlex

Background Patients with occlusion myocardial infarction (OMI) who meet the ST-elevation myocardial infarction (STEMI) criteria experience inequitable delays in care, because of sociodemographic factors, such as age and sex. OMI patients who do not meet STEMI criteria and are admitted to the hospital as non-STEMI patients, experience further delays. However, whether equity gaps exist in OMI care remains unknown. Methods A retrospective chart review included patients with acute coronary syndrome admitted to the hospital through 2 academic emergency departments, in the period from January 1, 2021 to December 31, 2022. Patients were categorized as having one of the following: OMI (acute culprit with Thrombolysis In Myocardial Infarction [TIMI] 0-2 flow, or acute culprit with TIMI 3 flow, and a troponin I level > 10,000 ng/L; or if they had no angiogram, a troponin I level > 10,000 ng/L plus new regional wall-motion abnormality on echocardiogram); non-OMI (MI that did not meet the OMI threshold); or MI ruled out. Results Among 662 charts, 260 were OMI patients, 296 were non-OMI patients, and 106 were patients with MI ruled out. Of the 260 OMI patients, 116 were admitted to the hospital as STEMI patients (true-positive), and 144 (55.4%) were admitted as non-STEMI patients (false-negative). In bivariate analyses, true-positive STEMI patients with atypical symptoms had a longer door-to-electrocardiogram (ECG) time ( P < 0.0001) and a longer ECG-to-catheterization time ( P < 0.001). False-negative STEMI patients had a longer door-to-ECG time for atypical symptoms ( P < 0.0001), a longer ECG-to-catheterization time for atypical symptoms ( P = 0.003), and were aged ≥75 years ( P = 0.006). Conclusions True-positive STEMI patients had delayed ECGs and catheterization for those presenting with atypical symptoms. More than half of those with OMI were admitted as non-STEMI patients, with further reperfusion delays for older patients and those presenting with atypical symptoms. Shifting to the OMI paradigm highlights reperfusion delays and equity gaps in the management of ACS.

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 score0.359
Threshold uncertainty score0.352

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 head0.059
GPT teacher head0.410
Teacher spread0.351 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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