Determining resectability inpancreatic tumors: Review of 70 cases
Bibliographic record
Abstract
Background: Endoscopic ultrasound (EUS) has gradually become the main stream method of the diagnosis and local treatment of pancreatic tumors. Endoscopic ultrasound (EUS) is frequently used in making the cytological diagnosis of pancreatic cancer and its great role in the pre-operative staging of pancreatic tumors.Objective: To evaluate the role of EUS in diagnosis and treatment of pancreatic tumors prospectively for 2 years study 2014-2015.Patients and methods: Prospective study including 70 patients who presented with pancreatic tumors underwent EUS at the endoscopy unit at Faculty of Medicine Cairo University and National Cancer Institute, Cairo University.Results: Out of 70 patients; median age was 55 years (range 32-73 years). Males were 32 (46%) and females were 38 (54%). Jaundice was the main symptom 47 (67%), clay colored stool 46 (65.7%), dark urine 47 (67%) and abdominal pain 50 (71%). There were 20 patients with benign disease and 50 patient with malignant disease. The following results showing the accuracy of the EUS in detecting malignant pancreatic tumors; Sensitivity: 96.0%, specificity: 75%, PPV: 90.6%, NPV: 88.2%, accuracy: 90.0%.Conclusion: EUS can clarify locoregional spread when CT/MR are equivocal. EUS Elastography is a new application in the field of the endosonography and seems to be able to differentiate fibrous and benign tissue from malignant lesions. The combination of superior detection, good staging, tissue diagnosis and potential therapy makes EUS guided FNA a cost-effective modality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".