Multislice computed tomography/contrast-enhanced ultrasound image fusion as a tool for evaluating unclear renal cysts
Bibliographic record
Abstract
Ultrasonography is a generally accepted imaging technique for diagnosing and monitoring cystic renal lesions. The widely used Bosniak classification (I-IV) categorizes renal cystic lesions into five distinctive groups according to ultrasonography and computed tomography (CT) image criteria. For solid renal lesions, determination of vascularity is discriminatory for malignancy in most instances. In indeterminate cases, contrast-enhanced ultrasound (CEUS) and magnetic resonance imaging/CT-ultrasound image fusion are able to detect and characterize difficult pathologies, with superior performance to either technique alone. In contrast to multislice CT (MS-CT), ultrasound image fusion is a real-time imaging technique that can be used in combination with other cross-sectional imaging modalities. This technical note describes state-of-the-art image fusion of CEUS and MS-CT to detect and characterize unclear renal pathologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".