Markers of Systemic Inflammation in Neuroendocrine Tumors
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to assess the impact of systemic markers of inflammation on the outcomes in patients with neuroendocrine tumors (NETs) treated with everolimus or placebo (as measured by baseline neutrophil-to-lymphocyte ratio [NLR] and lymphocyte-to-monocyte ratio [LMR]). METHODS: Patient data (gastrointestinal, pancreatic, and lung NETs) from 2 large phase 3 studies, RADIANT-3 (n = 410) and RADIANT-4 (n = 302), were pooled and analyzed. The primary end point was centrally assessed progression-free survival (PFS) as estimated by the Kaplan-Meier method. RESULTS: In the pooled population, elevated LMR (median PFS, 11.1 months; 95% confidence interval, 9.3-13.7; hazard ratio, 0.69; P < 0.001) and reduced NLR (median PFS, 10.8 months; 95% confidence interval, 9.2-11.7; hazard ratio, 0.75; P = 0.0060) correlated with longer PFS among all patients. These markers were also found to be prognostic in the everolimus- and placebo-treated subgroups. CONCLUSIONS: Data from this study suggest that LMR and NLR are robust prognostic markers for NETs and could potentially be used to identify patients who may receive or are receiving the most benefit from targeted therapies. As both are derived from a complete blood count, they can be routinely used in clinical practice, providing valuable information to clinicians and patients alike.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".