Biomarkers of systemic inflammation provide additional prognostic stratification in cancers of unknown primary
Bibliographic record
Abstract
BACKGROUND: Biomarkers of systemic inflammation have been shown to predict outcomes in patients with cancer of unknown primary (CUP). We sought to validate these findings in patients with confirmed CUP (cCUP) and explore their role alongside existing clinicopathological prognostic categories. PATIENTS AND METHODS: CUP oncologist from across the United Kingdom were invited to include patients with cCUP referred to their local CUP multidisciplinary team. Patient demographics, clinical, pathological and outcome data were recorded and analysed. RESULTS: Data were available for 548 patients from four CUP services. 23% (n = 124) of patients met clinicopathological criteria for favourable-risk cCUP. On multivariate analysis c-reactive protein (CRP) (p < 0.001) and the Scottish Inflammatory Prognostic Score (SIPS: combining albumin and neutrophil count) (p < 0.001) were independently predictive of survival. CRP and SIPS effectively stratified survival in patients with both favourable-risk and poor-risk cCUP based on clinicopathological features. CONCLUSIONS: Biomarkers of systemic inflammation are reliable prognostic factors in patients with cCUP, regardless of clinicopathological subgroup. We recommend that CRP or SIPS are incorporated into routine clinical assessments of patients with cCUP as a tool to aid investigation and/or treatment decision-making across all groups. Established clinicopathological factors can then be used to inform management pathways and specific systemic anticancer therapy selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".