Clinical Significance of GNAS Mutation in Intraductal Papillary Mucinous Neoplasm of the Pancreas With Concomitant Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to investigate the GNAS mutational status in pancreatic intraductal papillary mucinous neoplasm (IPMN) with and without distinct pancreatic ductal adenocarcinoma (PDAC) and to evaluate the significance of GNAS analysis using duodenal fluid (DF) in patients with IPMN. METHODS: The clinicopathologic features of 110 patients with IPMN including 16 with distinct PDAC were reviewed. The GNAS status in the IPMN tissue and 23 DF specimens was assessed by sensitive mutation scanning methods. RESULTS: The GNAS mutation rate in IPMN with distinct PDAC was significantly lower than that in IPMN without PDAC (4/16, 25%, vs 61/94, 65%; P = 0.0047). By multivariate analysis, GNAS wild-type and gastric type IPMNs were significantly associated with distinct PDAC. Of 45 GNAS wild-type IPMNs, 10 (43%) of 23 gastric type IPMNs had distinct PDAC, whereas only 2 (9%) of 22 non-gastric type IPMNs had distinct PDAC (P = 0.017). The GNAS status in DF was consistent with that in tissue in 21 (91%) of 23 patients. CONCLUSIONS: Distinct PDACs frequently develop in the pancreas with gastric type IPMN without GNAS mutations. Duodenal fluid DNA test would predict the GNAS status of IPMN, whereas the detection of the gastric subtype using noninvasive test remains to be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".