Recommendations on Robotic Hepato-Pancreato-Biliary Surgery. The Paris Jury-Based Consensus Conference
Bibliographic record
Abstract
OBJECTIVE: To establish the first consensus guidelines on the safety and indications of robotics in Hepato-Pancreatic-Biliary (HPB) surgery. The secondary aim was to identify priorities for future research. BACKGROUND: HPB robotic surgery is reaching the IDEAL 2b exploration phase for innovative technology. An objective assessment endorsed by the HPB community is timely and needed. METHODS: The ROBOT4HPB conference developed consensus guidelines using the Zurich-Danish model. An impartial and multidisciplinary jury produced unbiased guidelines based on the work of 10 expert panels answering predefined key questions and considering the best-quality evidence retrieved after a systematic review. The recommendations conformed with the GRADE and SIGN50 methodologies. RESULTS: Sixty-four experts from 20 countries considered 285 studies, and the conference included an audience of 220 attendees. The jury (n=10) produced recommendations or statements covering 5 sections of robotic HPB surgery: technology, training and expertise, outcome assessment, and liver and pancreatic procedures. The recommendations supported the feasibility of robotics for most HPB procedures and its potential value in extending minimally invasive indications, emphasizing, however, the importance of expertise to ensure safety. The concept of expertise was defined broadly, encompassing requirements for credentialing HPB robotics at a given center. The jury prioritized relevant questions for future trials and emphasized the need for prospective registries, including validated outcome metrics for the forthcoming assessment of HPB robotics. CONCLUSIONS: The ROBOT4HPB consensus represents a collaborative and multidisciplinary initiative, defining state-of-the-art expertise in HPB robotics procedures. It produced the first guidelines to encourage their safe use and promotion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".