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Record W4225315024 · doi:10.2217/mmt-2021-0005

Improved Overall Survival in Dual Compared to Single Immune Checkpoint Inhibitors in <i>BRAF</i> V600-Negative Advanced Melanoma

2022· article· en· W4225315024 on OpenAlexaff
Adi Kartolo, Cynthia Yeung, Markus Kuksis, Wilma M. Hopman, Tara Baetz

Bibliographic record

VenueMelanoma Management · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsKingston Health Sciences CentreQueen's University
Fundersnot available
KeywordsMelanomaMedicineHazard ratioRetrospective cohort studyAdverse effectInternal medicineOncologyImmune systemImmune checkpointGastroenterologyImmunotherapyCancerCancer researchImmunologyConfidence interval

Abstract

fetched live from OpenAlex

Aim: To evaluate the efficacy of dual versus single immune checkpoint inhibitors (ICI) in BRAF wild-type advanced melanoma patients. Materials & methods: A retrospective study of all advanced BRAF wild-type melanoma patients on palliative-intent ICI between 2015 and 2020 (n = 67). Results: Dual ICI had better overall survival (OS) when compared with single ICI in BRAF wild-type patients (hazard ratio: 0.204; 95% CI: 0.064–0.649; p = 0.007), but lost its statistical significance (median OSl not reached vs 20.9 months; p = 0.213; adjusted hazard ratio: 0.475; 95% CI: 0.164–1.380; p = 0.171) when only including patients treated after 2018 when dual ICI was funded in our province. Dual ICI were significantly associated with more frequent (p = 0.005) and severe (p = 0.026) immune-related adverse events, and required more immune-related adverse events-indicated systemic corticosteroid use (p < 0.001) compared with single ICI. Conclusion: While limited by small sample size and retrospective nature, dual ICI may have non statistically significant trend toward better OS efficacy when compared with single ICI in BRAF V600 wild-type advanced melanoma patients.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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.

Study designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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