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Record W2106887884 · doi:10.1517/14740330902808169

Ifosfamide nephrotoxicity in children: a mechanistic base for pharmacological prevention

2009· review· en· W2106887884 on OpenAlexaff
Lauren Hanly, Nancy Chen, Michael Rieder, Gideon Koren

Bibliographic record

VenueExpert Opinion on Drug Safety · 2009
Typereview
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsHospital for Sick ChildrenChildren's Hospital of Western OntarioUniversity of TorontoWestern University
FundersChinese Academy of Agricultural Sciences
KeywordsNephrotoxicityIfosfamideMedicinePharmacologyOxidative stressToxicityFanconi syndromeKidneyInternal medicineChemotherapyCisplatin

Abstract

fetched live from OpenAlex

The antineoplastic drug ifosfamide (IFO) in the treatment of solid tumors, particularly in children, is the cause of severe nephrotoxicity. Although it is a potent and effective chemotherapeutic agent, the associated nephrotoxicity has a serious impact on the health and the quality of life of exposed children. The toxic metabolite of IFO thought to be responsible for IFO-induced kidney damage is chloroacetaldehyde (CAA). Those suffering from nephrotoxicity typically develop tubular and glomerular toxicities, with the most severe form being Fanconi's syndrome. As the mode of toxicity of CAA seems to be primarily owing to oxidative stress, the use of antioxidants as a protective measure for the kidneys is a promising strategy. In this review, we highlight recent research that supports the local renal production of CAA as the proximate cause of IFO-induced nephrotoxicity with age as an important risk factor, those under the age of three being the most vulnerable. Most importantly, we focus on the potential advantages of the antioxidant N-acetylcysteine owing to both its antioxidant properties and its current use clinically in pediatrics.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.063
GPT teacher head0.402
Teacher spread0.339 · 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 designNot applicable
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

Citations47
Published2009
Admission routes1
Has abstractyes

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