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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".