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Record W3120979250 · doi:10.1097/spc.0000000000000536

Biological aspects of aging that influence response to anticancer treatments

2021· review· en· W3120979250 on OpenAlexaff
J. Crimmin, Tamàs Fülöp, Nicolò Matteo Luca Battisti

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2021
Typereview
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsUniversité de Sherbrooke
FundersNational Institute for Health and Care Research
KeywordsMedicineIntensive care medicineDiseaseCancerGeriatricsMultidisciplinary approachPopulation ageingPopulationAdverse effectBioinformaticsGerontologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Cancer is a disease of older adults, where fitness and frailty are a continuum. This aspect poses unique challenges to the management of cancer in this population. In this article, we review the biological aspects influencing the efficacy and safety of systemic anticancer treatments. RECENT FINDINGS: The organ function decline associated with the ageing process affects multiple systems, including liver, kidney, bone marrow, heart, muscles and central nervous system. These can have a significant impact on the pharmacokinetics and pharmacodynamics of systemic anticancer agents. Comorbidities also represent a key aspect to consider in decision-making. Renal disease, liver conditions and cardiovascular risk factors are prevalent in this age group and may impact the risk of adverse outcomes in this setting. SUMMARY: The systematic integration of geriatrics principles in the routine management of older adults with cancer is a unique opportunity to address the complexity of this population and is standard of care based on a wide range of benefits. This approach should be multidisciplinary and involve careful discussion with hospital pharmacists.

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)0.000
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 score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.202
GPT teacher head0.469
Teacher spread0.266 · 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 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

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