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Record W2887967651 · doi:10.5489/cuaj.5405

Trends and disparities in the use of neoadjuvant chemotherapy for muscle-invasive urothelial carcinoma

2018· article· en· W2887967651 on OpenAlexvenueno aff
Jonathan J. Duplisea, Ross Mason, Chad A. Reichard, Roger Li, Yu Shen, Stephen A. Boorjian, Colin P. Dinney

Bibliographic record

VenueCanadian Urological Association Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
FundersNational Cancer InstituteUniversity of Texas MD Anderson Cancer CenterNational Institutes of Health
KeywordsCystectomyMedicineBladder cancerReceiptChemotherapyLogistic regressionCancerInternal medicineOncologyUrology

Abstract

fetched live from OpenAlex

INTRODUCTION: Neoadjuvant chemotherapy (NAC) prior to radical or partial cystectomy is considered the standard of care for eligible patients with muscle-invasive urothelial carcinoma. Despite guideline recommendations, adoption of NAC has historically been low, although prior studies have suggested that use is increasing. In this contemporary study, we examine trends in the use of NAC and explore factors associated with its receipt. METHODS: We identified patients in the National Cancer Database who underwent radical or partial cystectomy for cT2-cT4N0M0 urothelial carcinoma from 2006-2014. The proportion of patients receiving NAC during each year was examined. Logistic regression models were used to evaluate clinical and socioeconomic factors associated with the receipt of NAC. RESULTS: A total of 18 188 patients were identified who underwent radical or partial cystectomy for muscle-invasive bladder cancer. Overall, 3940 (21.7%) received NAC. We noted a significant increase in the use of NAC over time, from 9.7% in 2006 to 32.2% in 2014. Factors associated with lower use of NAC include older age, higher comorbidity score, lower cT stage, lower hospital radical cystectomy volume, treatment at a non-academic facility, lower patient income, and receipt of partial cystectomy (all p<0.001). Interestingly, neither sex nor race were associated with receipt of NAC. CONCLUSIONS: Use of NAC has increased significantly over time to a modest rate of 32%. However, disparities still exist in the receipt of NAC, and future efforts aimed at mitigating these disparities are warranted.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.036
GPT teacher head0.264
Teacher spread0.229 · 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".

Quick stats

Citations43
Published2018
Admission routes1
Has abstractyes

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