The management of intraductal papillary mucinous neoplasms of the pancreas
Bibliographic record
Abstract
Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are one of the most common preneoplastic entities among pancreatic cystic neoplasms (PCN). Their incidence is increasing due to an extensive use of cross-sectional imaging, but management still remains controversial. Among IPMNs, the main duct (MD-IPMN) and mixed (MT-IPMN) types harbor a high risk of malignant degeneration requiring resection in most of cases. The branch duct type (BD-IPMN), on the other side, can be safely surveilled as surgical resection is limited to selected cases deemed at high risk of malignant progression according to specific clinical and radiological features. An accurate diagnosis and a correct assessment of malignant potential are often hard to achieve, and clinical management still relies on the experience of the gastroenterologist/surgeon that is called to choose between a major pancreatic resection burdened by high morbidity and mortality rates and a life-long surveillance. The purpose of this report is to summarize the available evidence supporting the current practice for the management of IPMN and to offer a useful practical guide from first observation to postoperative follow-up.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".