Updates on the Diagnostic Use of Ultrasonography Augmented With Perfluorobutane Contrast in Hepatocellular Carcinoma: A Meta-Analysis
Bibliographic record
Abstract
To investigate the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) in the diagnosis of primary hepatocellular carcinoma (HCC), a thorough search was conducted for pertinent literature using PubMed, SCOPUS, Web of Science, Science Direct, and Wiley Library. This was a meta-analysis of diagnostic test accuracy. MetaDiSc 1.4 was used for all analyses and assessed statistical heterogeneity with the I2 index and the chi-square test. The random-effects model was applied where there was considerable heterogeneity. Using the eight elements of the Newcastle-Ottawa Scale (NOS) for cohort and case-control studies, we assessed the quality of the included studies. Our results included nine studies with a total of 2598 patients, and 1607 (61.8%) were males. The pooled overall sensitivity of perfluorobutane with CEUS was 85.6% (95% CI 0.832, -0.878, and P=0.000) and specificity was 91.5% (95% CI 0.899, -0.930, and P=0.000) with significant inter heterogeneity between studies (I2=94.3% and 95.7%), respectively. The pooled positive likelihood ratio was 12.42 (4.59 to 33.61, P=0.000). Our analysis revealed a symmetric summary receiver operating characteristic (SROC) curve and seven of the included studies are near the top left corner of the graph, indicating that this test has a high diagnostic value. The results showed that CEUS augmented with perfluorobutane contrast had good diagnostic accuracy (sensitivity and specificity) for primary HCC. Further real-world data studies are needed to confirm the good diagnosis accuracy of perfluorobutane CEUS in primary HCC.
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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 | 0.027 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".