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Record W4309827972 · doi:10.1001/jamaoncol.2022.5486

Effectiveness of Adjuvant Pembrolizumab vs High-Dose Interferon or Ipilimumab for Quality-of-Life Outcomes in Patients With Resected Melanoma

2022· article· en· W4309827972 on OpenAlexaboutno aff
Joseph M. Unger, Amy K. Darke, Megan Othus, Thach‐Giao Truong, Nikhil I. Khushalani, Kari Kendra, Karl D. Lewis, Bryan A. Faller, Pauline Funchain, Elizabeth I. Buchbinder, Ahmad A. Tarhini, John M. Kirkwood, Elad Sharon, Vernon K. Sondak, Samantha Guild, Kenneth F. Grossmann, Antoni Ribas, Sapna P. Patel

Bibliographic record

VenueJAMA Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsMedicineIpilimumabPembrolizumabInternal medicineQuality of life (healthcare)Clinical endpointOncologyRandomized controlled trialRandomizationClinical trialAdjuvant therapyCancerImmunotherapy

Abstract

fetched live from OpenAlex

Importance: A key issue for the adjuvant treatment of patients with melanoma is the assessment of the effect of treatment on relapse, survival, and quality of life (QOL). Objective: To compare QOL in patients with resected melanoma at high risk for relapse who were treated with adjuvant pembrolizumab vs standard of care with either ipilimumab or high-dose interferon α 2b (HDI). Design, Setting, and Participants: The S1404 phase 3 randomized clinical trial was conducted by the SWOG Cancer Research Network at 211 community/academic sites in the US, Canada, and Ireland. Patients were enrolled from December 2015 to October 2017. Data analysis for this QOL substudy was completed in March 2022. Overall, 832 patients were evaluable for the primary QOL end point. Interventions: Patients were randomized (1:1) to treatment with adjuvant pembrolizumab vs standard of care with ipilimumab/HDI. Main Outcomes and Measures: Quality of life was assessed for patients at baseline and cycles 1, 3, 5, 7, and 9 after randomization using the Functional Assessment of Cancer Therapy (FACT) Biological Response Modifiers (FACT-BRM), FACT-General, Functional Assessment of Chronic Illness Therapy-Diarrhea, and European QOL 5-Dimension 3-Level scales. The primary end point was the comparison by arm of cycle 3 FACT-BRM trial outcome index (TOI) scores using linear regression. Linear-mixed models were used to evaluate QOL scores over time. Regression analyses included adjustments for the baseline score, disease stage, and programmed cell death ligand 1 status. A clinically meaningful difference of 5 points was targeted. Results: Among 1303 eligible patients (median [range] age, 56.7 [18.3-86.0] years; 524 women [40.2%]; 779 men [59.8%]; 10 Asian [0.8%], 7 Black [0.5%], 44 Hispanic [3.4%], and 1243 White [95.4%] individuals), 1188 (91.1%) had baseline FACT-BRM TOI scores, and 832 were evaluable at cycle 3 (ipilimumab/HDI = 267 [32.1%]; pembrolizumab = 565 [67.9%]). Evaluable patients were predominantly younger than 65 years (623 [74.9%]) and male (779 [58.9%]). Estimates of FACT-BRM TOI cycle 3 compliance did not differ by arm (ipilimumab/HDI, 96.0% vs pembrolizumab, 98.3%; P = .25). The adjusted cycle 3 FACT-BRM TOI score was 9.6 points (95% CI, 7.9-11.3; P < .001) higher (better QOL) for pembrolizumab compared with ipilimumab/HDI, exceeding the prespecified clinically meaningful difference. In linear-mixed models, differences by arm exceeded 5 points in favor of pembrolizumab through cycle 7. In post hoc analyses, FACT-BRM TOI scores favored the pembrolizumab arm compared with the subset of patients receiving ipilimumab (difference, 6.0 points; 95% CI, 4.1-7.8; P < .001) or HDI (difference, 17.0 points; 95% CI, 14.6-19.4; P < .001). Conclusions and Relevance: This secondary analysis of a phase 3 randomized clinical trial found that adjuvant pembrolizumab improved QOL vs treatment with adjuvant ipilimumab or HDI in patients with high-risk resected melanoma. Trial Registration: ClinicalTrials.gov identifier: NCT02506153.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.338
Teacher spread0.311 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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