Management of Pancreatic Cystic Lesions
Bibliographic record
Abstract
BACKGROUND: The prevalence of undefined pancreatic cystic neoplasms (PCNs) is high in the general population, increasing with patient age. PCNs account for different biological entities with different potential for malignant transformation. The clinician must balance his or her practice between the risk of surgical overtreatment and the error of keeping a malignant lesion under surveillance. METHODS: We review and discuss the clinical management of PCNs. Specifically, we analyze the main features of PCNs from the surgeon's point of view, as they present in the outpatient clinic. We also review the different consensus guidelines, address recent controversies in the literature, and present the current clinical practice at 4 different European Centers for pancreatic surgery. RESULTS: The main features of PCNs were analyzed from the surgeon's point of view as they present in the outpatient clinic. All aspects of surgical management were discussed, from indications for surgery to intraoperative management and surveillance strategies. CONCLUSIONS: Management of PCNs requires a selective approach with the aim of minimizing clinically relevant diagnostic mistakes. Through the evaluation of clinical and radiological features of a PCN, the surgeon can elaborate on a diagnostic hypothesis and assess malignancy risk, but the final decision should be tailored to the individual patient's need.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".