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Record W4383896923 · doi:10.1016/j.jaccao.2023.05.008

Cardiac Interventions in Patients With Active, Advanced Solid and Hematologic Malignancies

2023· review· en· W4383896923 on OpenAlexaff
Darryl P. Leong, Filipe Cirne, Nazanin Aghel, Rocío Consuelo Baro Vila, Germano Dallegrave Cavalli, Jeff S. Healey, Richard Whitlock, Dina Khalaf, Hira Mian, Sanjit S. Jolly, Shamir R. Mehta, Susan Dent

Bibliographic record

VenueJACC CardioOncology · 2023
Typereview
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicinePsychological interventionMalignancyIntensive care medicineMyocardial infarctionInternal medicineRandomized controlled trialCardiology

Abstract

fetched live from OpenAlex

Invasive cardiac interventions are recommended to treat ST-segment elevation myocardial infarction, non-ST-segment elevation acute coronary syndromes, multivessel coronary disease, severe symptomatic aortic stenosis, and cardiomyopathy. These recommendations are based on randomized controlled trials that historically included few individuals with active, advanced malignancies. Advanced malignancies represent a significant competing risk for mortality, and there is limited evidence to inform the risks and benefits of invasive cardiac interventions in affected patients. We review the benefit conferred by invasive cardiac interventions; the periprocedural considerations; the contemporary survival expectations of patients across several types of active, advanced malignancy; and the literature on cardiovascular interventions in these populations. Our objective is to develop a rational framework to guide clinical recommendations on the use of invasive cardiac interventions in patients with active, advanced cancer.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.365
Teacher spread0.307 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations27
Published2023
Admission routes1
Has abstractyes

Explore more

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