Serum Markers in Patients with Resectable Pancreatic Adenocarcinoma: Macrophage Inhibitory Cytokine 1 versus CA19-9
Bibliographic record
Abstract
PURPOSE: More accurate serum markers of pancreatic cancer could improve the early detection and prognosis of this deadly disease. We compared the diagnostic utility of a panel of candidate serum markers of pancreatic cancer. EXPERIMENTAL DESIGN: We collected preoperative serum from 50 patients with resectable pancreatic adenocarcinoma, as well as sera from 50 patients with chronic pancreatitis and 50 age/sex-matched healthy controls from our institution. Sera were analyzed for the following candidate markers of pancreatic cancer: CA19-9, macrophage inhibitory cytokine 1 (MIC-1), osteopontin, tissue inhibitor of metalloproteinase 1, and hepatocarcinoma-intestine-pancreas protein levels. RESULTS: By logistic regression analysis, MIC-1 and CA19-9 were significant independent predictors of diagnosis. Receiver operating characteristic curve analysis showed that MIC-1 was significantly better than CA19-9 in differentiating patients with pancreatic cancer from healthy controls (area under the curve is 0.99 and 0.78, respectively; P = 0.003), but not in distinguishing pancreatic cancer from chronic pancreatitis (area under the curve of 0.81 and 0.74, respectively; P = 0.63). Hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein, osteopontin, and tissue inhibitor of metalloproteinase 1 serum levels did not provide additional diagnostic power. CONCLUSION: In the differentiation of patients with resectable pancreatic cancer from controls, serum MIC-1 outperforms other markers including CA19-9.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".