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Quality of life, functioning, and symptoms in patients with previously treated locally advanced or metastatic urothelial carcinoma from EV-301: A randomized phase 3 trial of enfortumab vedotin versus chemotherapy.

2021· article· en· W3171965251 on OpenAlexaff
Ronac Mamtani, Jonathan E. Rosenberg, Thomas Powles, Guru Sonpavde, Yohann Loriot, Ignacio Durán, Jae‐Lyun Lee, Nobuaki Matsubara, Christof Vulsteke, Daniel Castellano, Srikala S. Sridhar, Helle Pappot, Begoña P. Valderrama, Howard Gurney, Jens Bedke, Michiel Simon Van Der Heijden, Chunzhang Wu, Zsolt Hepp, Caroline McKay, Daniel P. Petrylak

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineQuality of life (healthcare)DiscontinuationInternal medicineDocetaxelRandomized controlled trialPerformance statusOncologyCancer

Abstract

fetched live from OpenAlex

4539 Background: In EV-301, a randomized, open-label phase 3 study (NCT03474107), enfortumab vedotin (EV), a Nectin-4–directed therapy, significantly prolonged median overall survival by ̃3.9 months and reduced the risk of death by 30% compared with standard chemotherapy (SC; docetaxel, paclitaxel, or vinflunine) in patients with previously treated locally advanced/metastatic urothelial carcinoma. Understanding patient perspectives and experiences is important to further contextualize the benefits/risks of EV. Here, we report key prespecified quality-of-life (QoL) endpoints, a secondary objective of EV-301. Methods: Patients completed the validated European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire Core 30 (QLQ-C30) at baseline, on Day 1 of each week for the first 12 weeks, and then every 12 weeks until discontinuation. The QLQ-C30 assessed functional domains, symptom scales/items, financial impact, and overall health/QoL. Descriptive statistics were used to summarize instrument compliance rates and scores; mixed model repeated measures were used to evaluate changes from baseline over time. Logistic regressions were conducted to assess confirmed improvement rates, defined as clinically meaningful improvement (predefined per domain) over two subsequent visits. Results: Of the 608 randomized patients (EV, n = 301; SC, n = 307), 77.3% were male, median age was 68 (range: 30-88), and 30.9% had liver metastasis. Questionnaire compliance rates at baseline were ̃90% in both groups; during the study, average rates were 70.2% (EV) and 66.9% (SC). Baseline QLQ-C30 scores were similar between groups. At Week 12, scores on the global health status (GHS) scale were similar between groups (EV: -2.8, SC: -5.0; P=.2429), but SC was associated with numerically greater deterioration and more variability in QoL over the first 12 weeks. Patients receiving EV had significant reduction in pain symptoms (EV: -5.62, SC: +0.11; adjusted difference: -5.73, P<.05), but significant worsening of appetite loss (EV: +8.55, SC: +1.26; adjusted difference: 7.29, P<.05) compared with SC. Other symptom scores were not significantly different between groups. Higher proportions of patients on EV vs SC had significant confirmed improvements across all functioning domains (role, physical, emotional, social, cognitive), GHS, and several symptom scales (pain, fatigue, dyspnea, constipation). The greatest difference in improvement was reported for pain (EV: 51.6%, SC: 28.8%; OR = 2.76[1.81, 4.22]). Conclusions: Compared with SC, patients receiving EV had numerically less deterioration and variability in QoL during the first 12 weeks of treatment. More patients in the EV group had improvements over SC in 10 of 15 QLQ-C30 domains; improvement in pain showed the largest benefit. Clinical trial information: NCT03474107.

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)0.002
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.081
GPT teacher head0.434
Teacher spread0.353 · 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 designRandomized trial
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

Citations12
Published2021
Admission routes1
Has abstractyes

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