CA 19-9 as a biomarker in advanced pancreatic cancer patients randomised to gemcitabine plus axitinib or gemcitabine alone
Bibliographic record
Abstract
BACKGROUND: Response assessment in advanced pancreatic cancer (APC) is difficult and predictive markers are needed. There are insufficient data on the value of carbohydrate antigen 19-9 (CA 19-9) and cytostatic-targeted therapies. Axitinib, a selective vascular endothelial growth factor (VEGF) receptors 1, 2, 3 inhibitor, may increase overall survival (OS) in APC. METHODS: We assessed serum CA 19-9, clinical outcomes and diastolic blood pressure (dBP) in APC patients receiving gemcitabine plus axitinib (Gem+A) or gemcitabine alone. RESULTS: In the total population (N=95), median OS was significantly longer in patients with baseline CA 19-9 values at or below the median than in those with values above it (12.2 months [95% confidence interval (CI), 8.6-16.6%] vs 5.0 months [95% CI, 3.9-5.7%]; P<0.0001). This also reached significance in the Gem+A arm (median OS, 12.5 months [95% CI, 8.6-16.6%] vs 4.9 months [95% CI, 3.6-5.6%]; P<0.0001). Patients with any dBP>90 mmHg had significantly longer OS than those who did not. However, there was no predictive significance of CA 19-9. CONCLUSION: Baseline CA 19-9 levels had prognostic value for OS, but caution is advised in interpreting CA 19-9 as a predictive biomarker for novel cytostatic agents such as VEGF-targeted therapies in phase II studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".