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Record W4387341599 · doi:10.5489/cuaj.8570

The effect of cisplatin-based neoadjuvant chemotherapy on the renal function of patients undergoing radical cystectomy

2023· article· en· W4387341599 on OpenAlexaffvenue
Matthew D. Ho, Anna J. Black, Homayoun Zargar, Adrian Fairey, Laura S. Mertens, Colin P. Dinney, Maria Carmen Mir, Laura‐Maria Krabbe, Michael S. Cookson, Niels-Erik Jacobsen, Jeffrey S. Montgomery, Evan Y. Yu, Évanguelos Xylinas, Wassim Kassouf, Marc Dall’Era, Nikhil Vasdev, Srikala S. Sridhar, John S. McGrath, Jonathan Aning, Jeff M. Holzbeierlein, Andrew C. Thorpe, Shahrokh F. Shariat, Jonathan L. Wright, Todd M. Morgan, Trinity J. Bivalacqua, Scott North, Daniel A. Barocas, Yair Lotan, Petros Grivas, Andrew J. Stephenson, Jay B. Shah, Bas W. van Rhijn, Siamak Daneshmand, Philippe E. Spiess, Peter C. Black

Bibliographic record

VenueCanadian Urological Association Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer CentreMcGill University Health CentreUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsMedicineCystectomyRenal functionCohortBladder cancerCisplatinUrologyKidney diseaseRetrospective cohort studyInternal medicineContext (archaeology)ChemotherapyOncologySurgeryCancer

Abstract

fetched live from OpenAlex

INTRODUCTION: Cisplatin-based neoadjuvant chemotherapy (NAC) is the standard of care for patients with muscle-invasive bladder cancer (MIBC) undergoing radical cystectomy (RC). Cisplatin, however, can induce renal toxicity. Furthermore, RC is an independent risk factor for renal injury, with decreases in estimated glomerular filtration rate (eGFR) of up to 6 mL/min/1.73 m2 reported at one year postoperatively. Our objective was to evaluate the effect of cisplatin-based NAC and RC on the renal function of patients undergoing both. METHODS: We analyzed a multicenter database of patients with MIBC, all of whom received cisplatin-based NAC prior to RC. eGFR values were collected at time points T1 (before NAC), T2 (after NAC but before RC), and T3 (one year post-RC). eGFR and proportion of patients with eGFR <60 ml/min/1.73m2 (chronic kidney disease [CKD] stage ≥3) were compared between these time points. As all patients in this dataset had received NAC, we identified a retrospective cohort of patients from one institution who had undergone RC during the same time period without NAC for context. RESULTS: We identified 234 patients with available renal function data. From T1 to T3, there was a mean decline in eGFR of 17% (13 mL/min/1.73 m2) in the NAC cohort and an increase in proportion of patients with stage ≥3 CKD from 27% to 50%. The parallel cohort of patients who did not receive NAC was comprised of 236 patients. The mean baseline eGFR in this cohort was lower than in the NAC cohort (66 vs. 75 mL/min/1.73 m2). The mean eGFR decline in this non-NAC cohort from T1 to T3 was 6% (4 mL/ min/1.73 m2), and the proportion of those with stage ≥3 CKD increased from 37% to 51%. CONCLUSIONS: Administration of NAC prior to RC was associated with a 17% decline in eGFR and a nearly doubled incidence of stage ≥3 CKD at one year after RC. Patients who underwent RC without NAC had a higher rate of stage ≥3 CKD at baseline but appeared to have less renal function loss at one year.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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 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

Citations5
Published2023
Admission routes2
Has abstractyes

Explore more

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