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The Potential Role of Aerobic Exercise to Modulate Cardiotoxicity of Molecularly Targeted Cancer Therapeutics

2013· review· en· W2027587146 on OpenAlexafffund
Jessica M. Scott, Susan G. Lakoski, John R. Mackey, Pamela S. Douglas, Mark J. Haykowsky, Lee W. Jones

Bibliographic record

VenueThe Oncologist · 2013
Typereview
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsUniversity of Alberta
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of Canada
KeywordsCardiotoxicityMedicineCancerDiscontinuationAdverse effectCancer therapyDiseaseToxicityBioinformaticsPharmacologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Molecularly targeted therapeutics (MTT) are the future of cancer systemic therapy. They have already moved from palliative therapy for advanced solid malignancies into the setting of curative-intent treatment for early-stage disease. Cardiotoxicity is a frequent and potentially serious adverse complication of some targeted therapies, leading to a broad range of potentially life-threatening complications, therapy discontinuation, and poor quality of life. Low-cost pleiotropic interventions are therefore urgently required to effectively prevent and/or treat MTT-induced cardiotoxicity. Aerobic exercise therapy has the unique capacity to modulate, without toxicity, multiple gene expression pathways in several organ systems, including a plethora of cardiac-specific molecular and cell-signaling pathways implicated in MTT-induced cardiac toxicity. In this review, we examine the molecular signaling of antiangiogenic and HER2-directed therapies that may underpin cardiac toxicity and the hypothesized molecular mechanisms underlying the cardioprotective properties of aerobic exercise. It is hoped that this knowledge can be used to maximize the benefits of small molecule inhibitors, while minimizing cardiac damage in patients with solid malignancies.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.938
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.343
Teacher spread0.305 · 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 designOther design
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

Citations44
Published2013
Admission routes2
Has abstractyes

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