Endoscopic Ultrasound Versus CT Scan for Detection of the Metastases to the Liver
Bibliographic record
Abstract
BACKGROUND: Computed tomography (CT) scan is a standard test for the detection of the liver metastases; however, metastases are often missed on the CT scan. OBJECTIVE: To compare the accuracy of the endoscopic ultrasound (EUS)/endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) with CT scan for detection of the liver metastases. DESIGN: Prospective study. PATIENTS: Subjects with newly diagnosed tumors of the lung, pancreas, biliary tree, esophagus, stomach, and colon were enrolled. INTERVENTIONS: A CT scan and EUS examination of the liver was performed. EUS-FNA was performed on noncystic liver lesions. RESULTS: One hundred thirty-two cases were enrolled. The presence of liver metastasis was established in 26 cases. The diagnostic accuracy of EUS/EUS-FNA and CT scan was 98% and 92%, respectively (P=0.0578). In comparison to CT scan, EUS detected significantly higher number of metastatic lesions in the liver (40 vs.19; P=0.008). CT scan detected lesions in liver that were too small to be characterized in 8 cases (malignant-3; benign-5). Of these, EUS-FNA correctly characterized the lesion to be malignant in 3/3 cases and benign in 4/5 cases. No complications were observed as a result of EUS-FNA. LIMITATIONS: Endoscopist was not blinded to the findings of the CT scan. CONCLUSIONS: In comparison with the CT scan, there was trend in favor of EUS/EUS-FNA for the superior diagnostic accuracy. EUS was distinctly superior to the CT scan in detecting the number of metastatic lesions. EUS-FNA was also useful to identify the nature of lesions that were too small to be characterized on the CT scan.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".