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Record W2950035458 · doi:10.21037/atm.2019.05.51

Looking beyond cancer for cabozantinib-induced cardiotoxicity: evidence of absence or absence of evidence?

2019· letter· en· W2950035458 on OpenAlexaff
Eugenia Y. Lee, Andrew T. Yan

Bibliographic record

VenueAnnals of Translational Medicine · 2019
Typeletter
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsPazopanibCardiotoxicitySunitinibMedicineBevacizumabAxitinibCabozantinibOncologyInternal medicineIntensive care medicineCancerDiseaseTargeted therapyHeart failureVascular endothelial growth factorTyrosine-kinase inhibitorTyrosine kinaseVEGF receptorsChemotherapy

Abstract

fetched live from OpenAlex

The advent of molecular targeted therapy has transformed the scape of medical oncology in the past decades—patients previously deemed terminal now have more treatment options proven to extend survival. An example is renal cell carcinoma (RCC); given its myriad clinical presentation, a significant proportion of patients have locally advanced or metastatic disease at the time of diagnosis. Historically, treatment options for such patients were limited and their prognosis grim. In the past years, however, vascular endothelial growth factor (VEGF) inhibitors and tyrosine kinase inhibitors (TKIs), such as bevacizumab, sunitinib and pazopanib, have been shown to improve progression-free survival, although there have been increasing reports of drug cardiotoxicity including hypertension and heart failure (1-5). As a result, there is a growing need for better patient selection, prevention and monitoring of cardiotoxicity during treatment, as well as exploration of alternative safer agents.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.310
GPT teacher head0.436
Teacher spread0.126 · 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 designBench or experimental
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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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