Preliminary data from a multicenter Italian study: use of EUS-Elastography and Contrast in the differential diagnosis of SELs
Bibliographic record
Abstract
Aims Distinguishing gastrointestinal subepithelial lesions (GI-SELs) poses a clinical challenge because Endoscopic Ultrasound (EUS) is adept at detecting them but may fall short in providing effective differentiation. New methodologies have been introduced which provide further details and potential prognostic information. Elastography (EUS-E) allows to carry out a qualitative and semi-quantitative assessment of tissue stiffness, but for now only a few studies have examined its role in the diagnosis of SELs. Recent findings indicate that contrast agents enhance the diagnostic accuracy of EUS (CE-EUS) for SELs. The purpose of the study is to examine the performance of the EUS-E and CE-EUS in differentiating GI-SELs, and in particular gastrointestinal stromal tumors (GISTs).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".