Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine the threshold values and accuracy of 2 noninvasive techniques, contrast-enhanced ultrasound with maximum-intensity projection (MIP) imaging and ultrasound elastography, acoustic radiation force impulse (ARFI), to differentiate a normal from a cirrhotic liver. MATERIALS AND METHODS: One hundred thirty-two patients were predicted clinically as having a normal liver (n = 60) or cirrhosis (n = 72). All had MIP liver vessel evaluation on an Acuson Sequoia and 90 of them had ARFI on an S2000 (Siemens, Mountain View, Calif). Two readers reviewed 4 parameters on MIP data and predicted cirrhosis (n = 65) or normal (n = 67) outcome. They were considered as having cirrhosis when more than 1 MIP parameter was positive. Acoustic radiation force impulse values above 1.36 m/s suggested cirrhosis. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic accuracy of the ARFI measurement and to extract the optimal cutoff value in the differentiation of a cirrhotic liver from a noncirrhotic liver. RESULTS: Sixty-four patients (64/65, 98.5%) with abnormal and 8 (8/59, 13.6%) with normal vessels on MIP imaging were clinically cirrhosis (P < 0.001). Forty-five patients (45/49, 91.8%) with abnormal ARFI (≥ 1.36 m/s) and 6 (6/41, 14.6%) with normal ARFI (<1.36 m/s) had a clinical cirrhotic liver (P < 0.001). Sensitivity and specificity of MIP of vessel morphology to predict cirrhosis were 89% (64/72) and 98% (59/60), respectively. Acoustic radiation force impulse showed a sensitivity of 88% (45/51) and a specificity of 90% (35/39), and the area under the receiver operating characteristic curve was 0.932, suggesting very good accuracy. The combination of ARFI and MIP showed increased sensitivity and specificity to 95.8% and 100%. CONCLUSIONS: Liver evaluation with MIP and ARFI contributes to a noninvasive prediction of cirrhosis.
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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) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".