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Record W2936066104 · doi:10.14740/wjon1193

Gustave Roussy Immune Score and Royal Marsden Hospital Prognostic Score Are Biomarkers of Immune-Checkpoint Inhibitor for Non-Small Cell Lung Cancer

2019· article· en· W2936066104 on OpenAlexvenueno aff
Seigo Minami, Shouichi Ihara, Shouko Ikuta, Kiyoshi Komuta

Bibliographic record

VenueWorld Journal of Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineHazard ratioProportional hazards modelLung cancerOncologyMultivariate analysisPerformance statusCancerProgression-free survivalSurvival analysisLactate dehydrogenaseGastroenterologyOverall survivalConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: The Gustave Roussy Immune Score (GRIm-Score) and the Royal Marsden Hospital prognostic score (RMH score) were recently developed in order to improve a better participant selection for phase I trials. The GRIm-Score is formed by combination of lactate dehydrogenase (LDH), serum albumin concentration, and neutrophil-to-lymphocyte ratio (NLR). The RMH score is calculated by LDH, albumin, and number of metastases. These two scores have been validated only in phase I trials. The purpose of this study was to assess whether these scores are useful for practical treatment of immune-checkpoint inhibitor (ICI) monotherapy in pretreated non-small cell lung cancer (NSCLC). METHODS: This was a retrospective and single-centered study of 76 NSCLC patients treated with ICI monotherapy between December 2015 and October 2018 at our hospital. We divided 76 patients into high and low GRIm-Score and RMH score groups. Comparison of overall survival (OS) and progression free survival (PFS) was performed by Kaplan-Meier curves and log-rank tests. Independent prognostic factors of OS and PFS were analyzed by multivariate Cox proportional hazard analyses. RESULTS: The OS of the high GRIm-Score group was significantly shorter than that of the low score group (low vs. high; median 19.9 vs. 3.2 months, P < 0.01), while no significant difference was observed in PFS (2.6 vs. 2.1 months, P = 0.13). The PFS of the high RMH score was significantly shorter than that of the low score group (low vs. high; 2.6 vs. 1.8 months, P = 0.01), while there was no significant difference in OS (16.0 vs. 10.4, P = 0.24). Multivariate analyses detected high GRIm-Score (hazard ratio (HR) 3.93, 95% confidence interval (CI) 2.04 - 7.58, P < 0.01), and high RMH score (HR 1.76, 95% CI 1.03 - 3.02, P = 0.04) as poor prognostic factors of OS and PFS, respectively. CONCLUSIONS: Baseline GRIm-Score and RMH score were independent prognostic factors of OS and PFS of ICI monotherapy for pretreated NSCLC patients, respectively. These two scores are not only selection biomarkers for patients in experimental trials, but also useful prognostic biomarkers for NSCLC patients practically treated with ICI therapy.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.010
GPT teacher head0.267
Teacher spread0.256 · 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 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

Citations66
Published2019
Admission routes1
Has abstractyes

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