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Record W4407642584 · doi:10.1080/20450885.2025.2461963

Efficacy and safety of adjuvant systemic therapies in trial non-eligible resected stages III and IV melanoma patients

2025· article· en· W4407642584 on OpenAlexaffabout
Sarah Alsadiq, Adi Kartolo, Elaine McWhirter, Wilma Hopman, Tara Baetz

Bibliographic record

VenueMelanoma Management · 2025
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsQueen's UniversityMcMaster UniversitySaint John Regional Hospital
Fundersnot available
KeywordsMedicineMelanomaAdjuvantOncologyInternal medicineClinical trialSystemic therapyDermatologySurgeryCancerCancer research

Abstract

fetched live from OpenAlex

BACKGROUND: Adjuvant immunotherapy and targeted therapy are now the standard of care for patients with resected stage IIIA-IV melanoma. However, little is known regarding its efficacy in real-world patients who were not represented in these landmark trials. METHODS: This retrospective study included all patients with resected stage IIIA-IV melanoma who received adjuvant systemic therapy between January 1 2018 and December 31 2020, in two Canadian academic cancer. Primary outcome was the proportion of trial non-eligible patients in the real-world setting. Survival and safety analyses were also conducted. RESULTS: Of the total 113 patient, 99 (88%) were trial non-eligible patients. Most common reasons for trial non-eligible criteria was having no baseline CLND (72%), followed by outside of treatment window >12 weeks (30%), stage IIIA (14%), unknown primary (9%), stage IV (14%), and baseline AD on immunosuppressants (3%). There were no significant RFS (P = 0.731) or OS (P = 0.110) differences in the overall population of trial eligible vs. non-eligible. Safety profiles were similar between the trial eligible vs. non-eligible groups. CONCLUSION: Our study suggested a high proportion of real-world patients would have been deemed non-eligible for clinical trials. Regardless, adjuvant systemic therapy delivered similar survival and toxicity outcomes in both groups.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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