Prognostic Value of Performance Status, Albumin, and CRP in Last-Line Chemotherapy for Pancreatic vs. Other Gastrointestinal Cancers—Simple Tools Matter
Bibliographic record
Abstract
Patients with advanced gastrointestinal cancers often receive chemotherapy near the end of life (EoL), raising concerns about overtreatment. The PALLiON trial, a cluster-randomized trial, assessed the impact of a complex intervention on frequency of EoL treatment; the intervention involved palliative care referrals and the use of PROMs. The present secondary analysis evaluated the prognostic value of baseline performance status (PS), albumin (alb), C-reactive protein (CRP), and body mass index (BMI) for overall survival, comparing pancreatic (PAN, n = 189) vs. other gastrointestinal cancer patients (GI, n = 286). Baseline PS, alb, CRP, mGPS (modified Glasgow prognostic score), and BMI were analyzed using Cox regression. Adjusted for age, sex, and hospital size, PS ≥ 2 and alb < 35 g/L predicted shorter survival in both PAN and GI cancers, while CRP > 10 predicted shorter survival only in GI cancers. In PAN, PS ≥ 2 predicted a 78.4% higher probability of shorter survival, and mGPS 2 predicted a 68.7% higher probability. In GI, mGPS 2 predicted a 70.8% higher probability, whereas PS was not significant. BMI did not improve predictive models. PS ≥ 2 and low albumin are strong predictors of short survival in PAN, whereas increased CRP and low albumin (mGPS 2) are predictors in GI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| 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".