International multidisciplinary consensus recommendations on clinical application of three-dimensional visualization in precision surgery for pediatric liver tumors
Bibliographic record
Abstract
BACKGROUND: Pediatric liver tumors are predominantly primary malignant tumors, and complete tumor resection with sufficient preservation of liver tissue is crucial for improving prognosis. However, due to the delicate anatomical structure of the pediatric liver and the relatively large size of the tumors, especially in difficult cases, the surgical challenges are substantial. While precision liver surgery are widely applied in clinical practice, pediatric cases require more customized approaches. The application of three-dimensional (3D) visualization technology is crucial for enhancing surgical accuracy, allowing for precise preoperative planning and intraoperative guidance. METHODS: This consensus was collaboratively developed by 36 experts from eight countries, using the Glaser's state-of-the-art method to review and refine the draft. RESULTS: The final consensus resulted in 15 international multidisciplinary consensus recommendations on clinical application of 3D visualization in precision surgery for pediatric liver tumors. CONCLUSION: This consensus will standardize the application of 3D visualization technology in precision surgery for pediatric liver tumors to improve outcomes and reduce risks.
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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 | 0.101 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".