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Record W4411372338 · doi:10.1016/j.jaccao.2025.05.003

Lung Cancer and Cardiovascular Disease

2025· review· fi· W4411372338 on OpenAlex
Malak El-Rayes, Inbar Nardi Agmon, Christopher Yu, Nichanan Osataphan, Helena A. Yu, Andrew Hope, Adrian G. Sacher, Anthony F. Yu, Husam Abdel‐Qadir, Paaladinesh Thavendiranathan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJACC CardioOncology · 2025
Typereview
Languagefi
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsWomen's College HospitalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health NetworkToronto General HospitalCentre Integre de Sante et de Services Sociaux de Laval
FundersNational Cancer Institute
KeywordsDiseaseLung cancerMedicineCancerLungInternal medicineCardiologyIntensive care medicineOncology

Abstract

fetched live from OpenAlex

Among patients with cancer, those with lung cancer have the highest prevalence of pre-existing cardiovascular disease (CVD) and the highest risk of cardiovascular events postdiagnosis. This is driven by shared risk factors, particularly smoking and socioeconomic factors, and common biology. Furthermore, multimodality therapies for lung cancer, including surgery, radiation, chemotherapy, immunotherapy, and targeted therapy, are associated with CVD. Improvements in prevention, screening, and therapy for lung cancer have led to improved cancer survival, increasing the relevance of CVD for overall survival and quality of life. This review provides an overview of lung cancer and its treatment and discusses drivers of CVD, risk assessment, surveillance, prevention, and treatment strategies.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.384
Teacher spread0.361 · 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