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Epidemiologic Characteristics of Acute Kidney Injury During Cisplatin Infusions in Children Treated for Cancer

2020· article· en· W3023849385 on OpenAlexafffundabout
Kelly R. McMahon, Shahrad R. Rassekh, Kirk R. Schultz, Tom Blydt‐Hansen, Geoff D.E. Cuvelier, Cherry Mammen, Maury Pinsk, Bruce Carleton, Ross T. Tsuyuki, Colin J.D. Ross, Ana Palijan, Louis Huynh, Mariya Yordanova, Frédérik Crépeau-Hubert, Stella Wang, Debbie Boyko, Michael Zappitelli

Bibliographic record

VenueJAMA Network Open · 2020
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsHospital for Sick ChildrenQueen's UniversityUniversity of AlbertaCancerCare ManitobaUniversity of British ColumbiaMcGill University Health CentreMcGill UniversityMontreal Children's HospitalUniversity of TorontoCentre for Global Health ResearchBC Children's HospitalUniversity of Manitoba
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenMcGill University Health CentreMcGill University
KeywordsMedicineAcute kidney injuryCommon Terminology Criteria for Adverse EventsRenal functionKidney diseaseCreatinineInternal medicineCancerUrology

Abstract

fetched live from OpenAlex

Importance: Few multicenter pediatric studies have comprehensively described the epidemiologic characteristics of cisplatin-associated acute kidney injury using standardized definitions. Objective: To examine the rate of and risk factors associated with acute kidney injury among children receiving cisplatin infusions. Design, Setting, and Participants: This prospective cohort study examined children (aged <18 years) recruited from May 23, 2013, to March 31, 2017, at 12 Canadian pediatric academic health centers who were receiving 1 or more cisplatin infusion. Children whose estimated or measured glomerular filtration rate (GFR) was less than 30 mL/min/1.73 m2 or who had received a kidney transplant were excluded. Data analysis was performed from January 3, 2018, to September 20, 2019. Exposures: Cisplatin infusions. Main Outcomes and Measures: The primary outcome was acute kidney injury during cisplatin infusion, defined using a Kidney Disease: Improving Global Outcomes serum creatinine criteria-based definition (stage 1 or higher). The secondary outcome was acute kidney injury defined by electrolyte criteria from the National Cancer Institute Common Terminology Criteria for Adverse Events (grade 1 or higher). Assessments occurred at early (first or second cycle) and late (last or second to last cycle) cisplatin infusions. Results: A total of 159 children (mean [SD] age at early cisplatin infusion, 7.2 [5.3] years; 80 [50%] male) participated. The most common diagnoses were central nervous system tumors (58 [36%]), neuroblastoma (43 [27%]), and osteosarcoma (33 [21%]). Acute kidney injury (by serum creatinine level increase) occurred in 48 of 159 patients (30%) at early cisplatin infusions and 23 of 143 patients (16%) at late cisplatin infusions. Acute kidney injury (by electrolyte abnormalities) occurred in 106 of 159 patients (67%) at early cisplatin infusion and 100 of 143 patients (70%) at late cisplatin infusions. Neuroblastoma diagnosis and higher precisplatin GFR were independently associated with acute kidney injury (serum creatinine level increase) at early cisplatin infusions (adjusted odds ratio [aOR] for neuroblastoma vs other, 3.25; 95% CI, 1.18-8.95; aOR for GFR, 1.01; 95% CI, 1.00-1.03) and late cisplatin infusions (aOR for neuroblastoma vs other, 6.85; 95% CI, 1.23-38.0; aOR for GFR, 1.01; 95% CI, 1.00-1.03). Higher cisplatin infusion dose was also independently associated with acute kidney injury (serum creatinine level increase) at later cisplatin infusions (aOR, 1.05; 95% CI, 1.01-1.10). Conclusions and Relevance: The findings suggest that acute kidney injury is common among children receiving cisplatin infusions and that rate and risk factors differ at earlier vs later infusions. These results may help with risk stratification with a goal of risk reduction.

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)0.001
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 score0.067
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.029
GPT teacher head0.324
Teacher spread0.294 · 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".

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Citations36
Published2020
Admission routes3
Has abstractyes

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