Prognostic Value of Inflammation-Based Markers in Advanced or Metastatic Neuroendocrine Tumours
Bibliographic record
Abstract
Background: The role of systemic inflammation–based markers remains uncertain in advanced or metastatic neuroendocrine tumours (NETS). Methods: Systemic inflammatory factors, such as levels of circulating white blood cells and other blood components, were combined to yield inflammation-based prognostic scores [high-sensitivity inflammation-based Glasgow prognostic score (hSGPS), neutrophil:lymphocyte ratio (NLR), platelet:lymphocyte ratio (PLR), high-sensitivity inflammation-based prognostic index (hSPI), and prognostic nutritional index (PNI)], whose individual values as prognostic markers were retrospectively determined. Univariate and multivariate analyses were used to examine the association of inflammatory markers with overall survival (OS). Results: The study included 135 patients. Univariate analysis revealed that elevated white blood cell count, elevated neutrophil count, low serum albumin, elevated high-sensitivity C-reactive protein, and elevated hSPI, hSGPS, and NLR scores were significantly associated with worse OS. Multivariate analyses demonstrated that, apart from pathology grade and original site of the tumour, elevated hSPI (p = 0.004) was an independent prognostic factor for worse OS. Conclusions: In the present study, elevated pretreatment hSPI was observed to be an independent predictor of shorter OS in patients with inoperable advanced or metastatic NET. The hSPI might thus provide additional guidance for therapeutic decision-making in such patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".