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Record W3175091944 · doi:10.1186/s40959-021-00110-1

A new classification of cardio-oncology syndromes

2021· review· en· W3175091944 on OpenAlexaff
Rudolf A. de Boer, Joseph Pierre Aboumsallem, Valentina Bračun, Douglas Leedy, Richard K. Cheng, Sahishnu Patel, David Rayan, Svetlana Zaharova, Jennifer A. Rymer, Jennifer M. Kwan, Joshua Levenson, Claudio Ronco, Paaladinesh Thavendiranathan, Sherry‐Ann Brown

Bibliographic record

VenueCardio-Oncology · 2021
Typereview
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsToronto General Hospital
FundersH2020 European Research CouncilNational Center for Advancing Translational Sciences
KeywordsCancerMedicineDiseaseInternal medicineOncologyBioinformaticsBiology

Abstract

fetched live from OpenAlex

Increasing evidence suggests a multifaceted relationship exists between cancer and cardiovascular disease (CVD). Here, we introduce a 5-tier classification system to categorize cardio-oncology syndromes (COS) that represent the aspects of the relationship between cancer and CVD. COS Type I is characterized by mechanisms whereby the abrupt onset or progression of cancer can lead to cardiovascular dysfunction. COS Type II includes the mechanisms by which cancer therapies can result in acute or chronic CVD. COS Type III is characterized by the pro-oncogenic environment created by the release of cardiokines and high oxidative stress in patients with cardiovascular dysfunction. COS Type IV is comprised of CVD therapies and diagnostic procedures which have been associated with promoting or unmasking cancer. COS Type V is characterized by factors causing systemic and genetic predisposition to both CVD and cancer. The development of this framework may allow for an increased facilitation of cancer care while optimizing cardiovascular health through focused treatment targeting the COS type.

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0030.001
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 head0.113
GPT teacher head0.407
Teacher spread0.294 · 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 designNot applicable
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

Citations69
Published2021
Admission routes1
Has abstractyes

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