MétaCan
Menu
← Back to cohort

Trends over time in survival in patients with urothelial carcinoma in the real-world: A multicenter analysis.

2021· article· en· W3135052528 on OpenAlexaffabout
Sunil Samnani, Zachary Veitch, Jeenan Kaiser, Carlos Stecca, Husam Alqaisi, Scott North, Srikala S. Sridhar, Nimira Alimohamed

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of AlbertaSt. Michael's HospitalUniversity Health NetworkBaker Hughes (Canada)
Fundersnot available
KeywordsMedicineBladder cancerInternal medicineMultivariate analysisMetastatic Urothelial CarcinomaOncologyCancerUrothelial cancerChemotherapyDiseaseUrothelial carcinoma

Abstract

fetched live from OpenAlex

412 Background: Patients with muscle-invasive bladder cancer (MIBC) historically have poor long-term outcomes, with nearly 50% developing metastatic disease. Similarly, patients with metastatic urothelial carcinoma (mUC) have had median overall survivals of less than 2 years. Novel therapies have been implemented over time in attempts to improve outcomes. This study evaluates trends in survival over time in patients with MIBC and mUC treated in the real-world setting. Methods: Retrospective data was collected from two major cancer centres in Alberta and the Princess Margaret Cancer Centre in Ontario, Canada. Consecutive patients treated with platinum-based chemotherapy between 01/2005 and 01/2018 who had confirmed MIBC or mUC were evaluated. Patients were excluded if they had been treated as part of a clinical trial in the first-line setting. Patients were categorized based on year of diagnosis at presentation: time period 1 (T1) diagnosed between 01/2005 and 12/2011, and time period 2 (T2) diagnosed between 01/2012 and 12/2018. The co-primary endpoints were disease-free survival (DFS) for MIBC, progression-free survival (PFS) for mUC, and overall survival (OS) for both. Results: 572 patients were included, 196 (78% male; median age 63.8 years) had MIBC and 376 (76% male; median age 68.4 years) were treated for mUC. Amongst patients with MIBC, 33% (65) were treated in T1 and 67% (131) in T2. Median DFS and OS were significantly improved in T2 compared to T1 for patients with MIBC (Table). On multivariate analysis, earlier year of diagnosis and ECOG status ≥2 was independently associated with poor outcomes (p=0.016 and p=0.008, respectively). Amongst patients with mUC, 205 (55%) were treated in T1 and 171 (45%) in T2. Median PFS and OS did not significantly improve over time in patients with mUC from T1 to T2 (Table). Conclusions: In this real-world analysis, outcomes for patients with MIBC have significantly improved over time. This is likely attributed to standardization of perioperative chemotherapy protocols and improvements in surgical techniques. Similar improvements have not yet been demonstrated for patients with mUC during the two time periods. However, novel therapies (eg. immunotherapy) were only approved in 2017. Future analysis may explore the reasons for improvement in patients with MIBC and will evaluate outcomes in mUC patients treated from 2017 onwards. CI= confidence interval, HR= hazard ratio. [Table: see text]

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)0.002
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 score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.054
GPT teacher head0.422
Teacher spread0.368 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicBladder and Urothelial Cancer Treatments→French-language works237,207→