Diagnosis of Focal Liver Masses on Ultrasonography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it