Identifying Contraindication to Resection in Patients with Pancreatic Carcinoma: The Role of Endoscopic Ultrasound
Bibliographic record
Abstract
OBJECTIVE: To present recently published material comparing the performance of endosonography relative to other imaging modalities when evaluating the patient with a suspected or known pancreas carcinoma. METHODS: Medline was searched using the terms "endosonography" and "pancreas neoplasms". References from retrieved papers were reviewed to identify other reports. Emphasis was placed on peer-reviewed material published within the past three years that included comparison with other imaging modalities. RESULTS: Despite advances in cross-sectional imaging modalities, endosonography remains the most sensitive and specific method for identifying pancreatic mass lesions. The resectability of pancreatic carcinoma is best determined with dual-phase helical computed tomography, although endosonography may be slightly more accurate for lymph node assessment. Endoscopic ultrasound-guided fine needle aspiration biopsy has a high sensitivity (93%) and specificity (100%) when used in patients with masses in whom pancreatic cancer is suspected but prior biopsies have been negative. CONCLUSIONS: Endosonography helps in the diagnosis of pancreatic neoplasms through definitive inclusion or exclusion of a mass lesion as well as biopsy confirmation of malignancy. The role of endosonography in the determination of resectability has been eclipsed by dual-phase helical computed tomography. However, endoscopic ultrasound with fine needle aspiration of nonperitumoral lymph nodes may identify advanced disease with sufficient frequency to justify its routine use in patients with lesions that are thought to be resectable based on helical computed tomography.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".