Diagnosis of Focal Liver Masses on Ultrasonography
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare the diagnostic accuracy, confidence level, and recommended management of focal liver masses after contrast-enhanced ultrasonography (CEUS) compared with unenhanced ultrasonography alone. METHODS: One hundred sixty-seven patients were referred for CEUS to characterize a focal liver mass. A 2-person blind read determined benignancy or malignancy, comparative diagnosis, and accuracy on both ultrasonography and CEUS. Results were compared with the final diagnoses. RESULTS: The 2 readers could not determine benignancy or malignancy in 77 (46.1%) and 46 (27.5%) of 167 unenhanced scans compared with 2 (1.2%) and 1 (0.6%) of 167 CEUS scans. The confidence level increased from 0 responses in the 2 highest grades (4 and 5) on the unenhanced scans to 135 (81.8%) and 132 (79.5%) of 167 at level 5 for CEUS. Regarding the diagnosis, the confidence level was lowest (grade 1) on the unenhanced scans in 128 (82.1%) and 79 (65.3%) of 167 for the 2 readers and improved to the highest (grade 5) in 110 (65.9%) and 113 (68.1%) of 167. Regarding diagnostic accuracy, the unenhanced scans agreed with the correct diagnosis in 85 (50.9%) and 63 (37.7%) of 167, and CEUS agreed with the correct diagnosis in 133 (79.6%) and 142 (85%) of 167 for readers 1 and 2, respectively. Recommendations for further imaging decreased from 166 (99.4%) and 147 (88%) of 167 on the unenhanced scans to 30 (18%) and 5 (3%) of 167 on CEUS for readers 1 and 2. CONCLUSIONS: Contrast-enhanced ultrasonography improves the accuracy and confidence of diagnosis of focal liver lesions and reduces recommendations for further investigations.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".