Prospective Cross-Sectional Study of the Prevalence of Incidental Pancreatic Cysts During Routine Outpatient Endoscopic Ultrasound
Bibliographic record
Abstract
OBJECTIVE: Incidental pancreatic cysts are often detected during abdominal imaging and require follow-up since some have malignant potential. Endoscopic ultrasound (EUS) is highly sensitive for pancreatic diseases, yet the prevalence of incidental pancreatic cysts discovered with EUS is unknown. The objective of the study was to determine its prevalence by EUS. METHODS: A prospective cross-sectional study was conducted. Patients undergoing EUS for nonpancreatic indications and without known pancreatic abnormality were recruited to assess the prevalence of pancreatic cysts and its characteristics. Risk factors were determined by logistic regression. RESULTS: We enrolled 341 patients (mean age, 59 years; 187 females) and found 46 incidental pancreatic cysts (median [range], 5 [2-80] mm) in 32 patients (9.4%). Branch duct intraductal papillary mucinous neoplasm was the most common finding. Seven cysts were larger than 1 cm and 1 adenocarcinoma was discovered. Multivariate logistic regression showed an association between pancreatic cysts and older age (odds ratio, 1.04 per year; 95% confidence interval, 1.01-1.08) and female sex (odds ratio, 3.08; 95% confidence interval, 1.25-7.45). CONCLUSIONS: In our population, the prevalence of incidental pancreatic cyst discovered on EUS was 9.4% and the majority were less than 1 cm. Increasing age and female sex were associated with the development of pancreatic cysts.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".