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Record W3047541532 · doi:10.34172/jrip.2020.29

Does exercise training attenuate cisplatin nephrotoxicity?

2020· article· en· W3047541532 on OpenAlexaff
Mina Kafashi, Mohammad Reza Kaffashian, Mehdi Nematbakhsh, Maryam Maleki, Tahereh Safari

Bibliographic record

VenueJournal of Renal Injury Prevention · 2020
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsCanadian Society for Exercise Physiology
FundersIlam UniversityZahedan University of Medical SciencesIlam University of Medical Sciences
KeywordsMedicineNephrotoxicityKidney diseaseWastingCisplatinPhysical exerciseModerate exerciseDiseaseImmune systemRenal functionPharmacologyKidneyInternal medicineChemotherapyImmunology

Abstract

fetched live from OpenAlex

Cisplatin (CP), a medication originating from the platinum has been used for solid cancers’ treatment in the last decade. CP is associated with numerous side effects as well. One of the side effects is nephrotoxicity. There are some types of procedure which can attenuate harmful effects of the drug, and the effectiveness of physical activity has been a controversial topic. It is well established that physical activity has positive effects on chronic kidney disease (CKD). The exercise training can modulate CP induced muscle wasting both in males and females. Although exercise training may have protective effect on renal function and the related risk factors, it cannot attenuate the renal injury resulted from CP therapy in females. The exercise training may improve interleukin 6 and heme oxygenase-1, reduces the production of CD4+T cell cytokines from the kidney, which play a major role in adaptive immune response. The present mini-review considered the effect of exercise training accompanied by the CP treatment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.308
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes1
Has abstractyes

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