Accuracy of Endoscopic Ultrasound in the Evaluation of Cystic Pancreatic Neoplasms
Bibliographic record
Abstract
OBJECTIVES: Reports on the use of endoscopic ultrasound (EUS) in differentiating benign, premalignant, and malignant pancreatic lesions have been widely variable, particularly with cystic neoplasms. We evaluated the use of EUS for cystic pancreatic lesions in a community hospital setting. METHODS: All patients who underwent EUS for cystic pancreatic neoplasms from 2007 to 2010 were reviewed. A final EUS diagnosis was determined based on the examiner's impression and fine-needle aspiration results if available. Lesions were stratified as benign, premalignant, or malignant. Patients underwent surgical resection, serial imaging studies, or medical oncology/palliative care consultation as indicated. RESULTS: One hundred eighteen patients with cystic pancreatic lesions underwent EUS during the study period. Endoscopic ultrasound diagnoses included 75 benign (63.6%), 35 premalignant (29.7%), and 8 malignant (4.2%) lesions. Thirty-eight patients (32.2%) underwent surgery, 77 (65.3%) were monitored with imaging, and 3 (2.5%) had unresectable malignancies. Elevated carcinoembryonic antigen levels showed a trend toward predicting mucinous cysts (P = 0.062). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for cystic lesions were 87.3%, 86.8%, 87.5%, 76.7%, and 93.3%, respectively. CONCLUSIONS: Endoscopic ultrasound is a valuable diagnostic modality in the evaluation of cystic pancreatic neoplasms in a community hospital setting.
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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 | 0.003 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".