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Cardiotoxicity of antitumor therapy in children. Literature review

2025· article· en· W4412659457 on OpenAlexaff
L. R. Turkiya

Bibliographic record

VenueMedical alphabet · 2025
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsNational Defence Medical Centre
Fundersnot available
KeywordsCardiotoxicityMedicineIntensive care medicineOncologyInternal medicinePharmacologyChemotherapy

Abstract

fetched live from OpenAlex

Relevance. Cardiotoxicity of antitumor therapy in children represents the important problem in pediatric oncology. In recent decades, due to the introduction of new treatment methods, there has been a significant increase in the survival rate of children with oncologic diseases, but cardiovascular complications developing during antitumor treatment become a frequent problem and have a serious impact on the quality of life of patients, as well as may increase the risks of lethal outcomes. Objective. To review the existing data on cardiotoxicity resulting from the use of antitumor drugs in children, with emphasis on the mechanisms of its development, clinical manifestations, diagnostic methods, approaches to treatment and prevention. Materials and Methods. When writing the literature review, data were analyzed in databases PubMed, Scopus, eLIBRARY devoted to the problem of cardiotoxicity of antitumor therapy in children for the period 2013–2024. Conclusion. Cardiotoxicity remains an important and underestimated problem in pediatric oncology. Objective assessment of cardiovascular risk before, during and after chemotherapy, timely diagnosis of complications and implementation of cardioprotection methods can significantly improve the quality and duration of life of children with malignant diseases. Further research is needed to develop standardized protocols for screening and prevention of cardiotoxicity in these children.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.289
Teacher spread0.283 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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