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Cardiac care after myocardial infarction in cancer survivors: A population-based study.

2015· article· en· W2939993825 on OpenAlexaffabout
Kelvin Chan, Andrew T. Yan, Winson Y. Cheung, Craig C. Earle, Dennis T. Ko

Bibliographic record

VenueJournal of Clinical Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsBC Cancer AgencySt. Michael's HospitalHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMyocardial infarctionPsychological interventionInternal medicineCohortCancerPopulationEmergency medicineSurgery

Abstract

fetched live from OpenAlex

9578 Background: Cancer survivors (CS) may receive suboptimal non-cancer related care, such as lower rates of bypass surgery after myocardial infarction (MI) in the US. Secondary prevention (SP) after MI is an important aspect of survivorship care. We aim to examine the use of medications and interventions for SP after MI in CS vs. non-cancer patients (NCP). Methods: All acute MI patients (pts) hospitalized in Ontario between 1995 and 2012 were identified from the Canadian Institute of Health Information databases, and linked to the Ontario Cancer Registry to determine whether they were CS or NCP. Those who were diagnosed with cancer < 1 year before their MI were excluded. The cohort was linked to other administrative databases to determine demographics, comorbidities, cardiac risk factors, hospital-based interventions and, for those over age 65, outpatient-based medication use. Propensity scores derived from baseline characteristics were used to create a 1:4 (CS:NCP) matched cohort. The use of medications and interventions within the first 90 days of MI, and medication adherence (measured by proportion of days covered (PDC) within the first year of MI) were compared between CS and NCP using matched analyses. Results: We identified 102,415 MI pts (CS = 20,483; NCP = 81,932) with 57% male and 86% > age 65. Slightly fewer CS vs. NCP received angiograms (37.4% vs. 38.6%; p = 0.003) and percutaneous coronary interventions (17.0% vs. 17.8%; p = 0.01), but similar CS and NCP received bypass surgery (2.5% vs. 2.5%). For pts who were > age 65 and active users of the public drug programs with MI < 5 years after cancer diagnosis, fewer CS vs. NCP received ACEi/ARB (67.0% vs. 70.6%; p < 0.001), statins (54.9% vs. 60.0%; p < 0.001), and clopidogrel (27.8% vs. 33.8%; p < 0.001), but the differences for those with MI > 5 years after cancer diagnosis were much less (all interaction p < 0.01). Similar CS and NCP received beta-blockers and nitrates. Both groups had similar degree of medication adherence, except for ACEi/ARB (PDC: 76.0% (CS) vs. 76.9% (NCP), p = 0.03). Conclusions: Slightly fewer CS than NCP received SP after MI, especially for those occurred within 5 years after cancer diagnosis. Further studies are needed to examine the outcome implications of this finding.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.115
GPT teacher head0.469
Teacher spread0.353 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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