Predictive value of cystic fibrosis transmembrane conductance regulator (CFTR) in the diagnosis of gastric cancer
Bibliographic record
Abstract
PURPOSE: Gastric cancer is associated with poor prognosis. The high mortality rate of gastric cancer is mainly attributed to late detection, so diagnosis and treatment are crucial to decreasing mortality. The purpose of this study was to examine the predictive accuracy and discriminative ability of cystic fibrosis transmembrane conductance regulator (CFTR) in gastric cancer patients, in addition to the classical cancer tumor biomarkers carbohydrate antigen 199 (CA199) and carcinoembryonic antigen (CEA). METHODS: The study was performed on 78 serum samples from gastric cancer patients and 88 serum samples from healthy adults. Serum levels of CFTR, CA199, CEA and CHN were determined by enzyme-linked immunosorbent assay (ELISA) Results: Spearman's coefficient analysis showed that, in some cases, CFTR was strongly correlated with CA199 and that CFTR levels increased with age. Kruskal-Wallis testing indicated concentrations of CFTR and CA199 had statistically significant association with stage. Logistic regression showed that CFTR and CA199 independently predicted gastric cancer. Receiver operating characteristics (ROC) showed that combinations of CFTR, CA199, and CEA yielded the best ROC curve, with an AUC of 0.875. CONCLUSIONS: The results of this study indicate that the serum CFTR has a broad application prospects for detection of GC.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".