Combination of preoperative CEA and CA19-9 improves prediction outcomes in patients with resectable pancreatic adenocarcinoma: results from a large follow-up cohort
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with a 5-year survival rate of <7%. Carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) are often used to predict the outcome of the malignancy independently. However, the joint prognostic effect of the two tumor biomarkers has not been well determined. The study assessed the joint role of preoperative CA19-9 and CEA in the prognostic prediction of resectable PDAC in a large cohort of patients. The study enrolled 460 eligible patients who were ready to undergo surgery for PDAC. Restricted cubic spline and direct-adjusted survival curve revealed the nonlinear association between the biomarker levels and prognosis of patients. Combination of preoperative CA19-9 and CEA effectively improved the prognostic prediction. About 100 U/mL of CA19-9 and 10 μg/mL of CEA were revealed as potential assistant index for prognostic prediction in patients with resectable PDAC and may be used as one of the criteria to assess the resectability of PDAC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".