Gustave Roussy Immune Score and Royal Marsden Hospital Prognostic Score Are Prognostic Markers for Extensive Disease of Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: The Royal Marsden Hospital prognostic score (RMH score) and the Gustave Roussy immune score (GRIm-score) were developed in order to select more suitable patient for phase I trials. Lactate dehydrogenase (LDH) and serum albumin concentration are common risk factors to these two systems. As the third risk factor, the RMH score and the GRIm-score adopt number of metastatic sites and neutrophil-to-lymphocyte ratio (NLR), respectively. We aimed to investigate whether these two systems are also useful for extensive disease of small cell lung cancer (ED-SCLC). METHODS: We retrospectively collected 128 patients who had initiated platinum-based chemotherapy at our hospital between September 2007 and March 2018. We divided our patients into low (score 0 - 1) and high (2 - 3) score groups, and compared overall survival (OS) and progression-free survival (PFS) between them. Multivariate Cox proportional hazard analyses found prognostic factors of survival times. RESULTS: Regarding GRIm-score, OS was significantly shorter in high score group than in low score group (median 6.1 vs. 11.4 months, P < 0.01), while no significant difference was observed in PFS (median 4.7 vs. 5.0 months, P = 0.12). Both OS (median 6.9 vs. 12.4 months, P < 0.01) and PFS (median 4.4 vs. 5.4 months, P = 0.01) were significantly shorter in high RMH score group than in low group. Multivariate analyses detected both high GRIm-score (hazard ratio (HR) 1.80, 95% confidence interval (CI) 1.20 - 2.72, P < 0.01) and high RMH score (HR 1.93, 95% CI 1.27 - 2.92, P < 0.01) as independent worse prognostic factors of OS, and then only high RMH score (HR 1.53, 95% CI 1.04 - 2.25, P = 0.03) as independent worse prognostic factor of PFS. CONCLUSIONS: Both RMH score and GRIm-score are useful as independent prognostic factors of OS in ED-SCLC. However, only RMH score is an independent prognostic factor of PFS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".