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Record W3127379102 · doi:10.1097/cji.0000000000000360

Impact of Baseline Corticosteroids on Immunotherapy Efficacy in Patients With Advanced Melanoma

2021· article· en· W3127379102 on OpenAlexaffabout
Adi Kartolo, Jasna Deluce, Ryan Holstead, Wilma M. Hopman, John Lenehan, Tara Baetz

Bibliographic record

VenueJournal of Immunotherapy · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsMedicinePrednisoneHazard ratioInternal medicineConfidence intervalCorticosteroidOncologyImmunotherapyRetrospective cohort studyCancerSurgery

Abstract

fetched live from OpenAlex

This is a 2-center, retrospective study which aimed to evaluate the effect of baseline corticosteroid use on immunotherapy efficacy in patients with advanced melanoma. We included all patients with advanced unresectable and metastatic melanoma on single-agent programmed cell death protein 1 (PD-1) inhibitors at the Cancer Centre of Southeastern Ontario and London Regional Cancer Program. We defined baseline corticosteroid use as prednisone-equivalent of ≥10 mg within 30 days of immunotherapy initiation. Our study had 166 patients in total, and 25 were taking corticosteroids at the initiation of the PD-1 inhibitor. Baseline prednisone-equivalent ≥10 mg did not have effect on median overall survival (hazard ratio=1.590, 95% confidence interval: 0.773-3.270, P=0.208). However, a higher dose of baseline prednisone-equivalent ≥50 mg was independently associated with poor median overall survival (hazard ratio=2.313, 95% confidence interval: 1.103-4.830, P=0.026) when compared with baseline prednisone-equivalent 0-49 mg, even when controlled for confounders including baseline Eastern Cooperative Oncology Group ≥2 and baseline brain metastasis. Consideration should be made to decrease the use of unnecessary steroids as much as possible before initiation of PD-1 inhibitor treatment.

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 score0.601
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.285
Teacher spread0.277 · 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

Citations22
Published2021
Admission routes2
Has abstractyes

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