Synoptic Reporting of Focal Liver Masses in at Risk Patients
Bibliographic record
Abstract
OBJECTIVES: Reporting contrast-enhanced ultrasound (CEUS) for focal liver masses in at risk patients is a challenging task. Traditionally used prose reporting (PR) is inconsistent and lacks standardization. We propose synoptic reporting (SR), encompassing algorithmic interpretation and liver imaging and reporting data system (LI-RADS) categorization. METHODS: A software worksheet from Kailo Medical (Melbourne, AU), incorporates the CEUS algorithm for liver interpretation and CEUS LI-RADS categorization. Part 1. Feasibility of SR: twenty participants of varying experience were presented a brief lecture on SR, algorithmic approach to liver mass interpretation, and CEUS LI-RADS categorization. Ten representative liver masses were shown as unknown cases. Participants inputted data into SR worksheets. Results and LI-RADS category were generated solely by SR. Data were categorized as "correct" or "incorrect." Part 2. Prospective Analysis: Ninety-one patients for SR and 56 for PR, all were tested for completeness, efficiency, and user satisfaction. RESULTS: Part 1: Junior participants, pass rate 81.6%, and senior participants, pass rate 83.3% showed no difference in performance. Part 2: Completeness: SR 98.4% and PR 87.0%. Efficiency: Average total time to completion: SR 11 minutes and PR 20 minutes. User satisfaction: Ultrasound technologists, all referring physicians, and six out of seven radiologists preferred SR over PR. Major benefits cited were total time saved, consistency and accuracy in documentation, and report completeness. CONCLUSIONS: SR is a reliable and useful tool in clinical practice to report liver masses on ultrasound and assign an appropriate LI-RADS categorization and management pathway. This ultimately improves communication with referring clinicians and leads to better patient outcomes.
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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 | 0.028 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".