Evaluation of a New Tumor-Associated Antigen in Pancreatic Cancer
Bibliographic record
Abstract
This study was carried out to assess the diagnostic accuracy of a new gastrointestinal cancer antigen ( GICA ) defined by a monoclonal antibody. Its sensitivity and specificity were assayed in a group of patients with different pancreatic diseases (10 acute pancreatitis, 27 chronic pancreatitis, 22 cancers of the pancreas) and in 29 normal individuals. The concentration of GICA was always inferior to 37 units/ml (our discriminant limit between cancer and noncancer patients) both in cases with chronic pancreatitis and in healthy subjects. Increased levels of the antigen were found in 16/22 (72.7%) pancreatic cancer patients and in 3/10 (33.3%) cases with acute pancreatitis. The assay was within the normal range in 2 (28.6%) out of 7 cancers judged resectable. The test is simple and rapid, but its relative sensitivity and the frequent elevation of GICA in other adenocarcinomas of the gastrointestinal tract make it unsuitable for screening programs in pancreatic cancer. Even its use for early diagnosis of cancer of the pancreas does not seem promising. The major finding of our study is the lack of false-positives in patients with chronic pancreatitis and therefore the usefulness of this test in differentiating preoperatively between chronic inflammation and cancer of the gland. Frequent increase of the marker in patients with acute pancreatitis is not yet clear.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".