Assessing Surgical Innovation
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of the IDEAL (innovation, development, exploration, assessment and long-term) paradigm on the development of ALPPS (associating liver partition and portal vein ligation for staged hepatectomy) in comparison to the evaluation of 2 other revolutionary innovations: laparoscopic cholecystectomy (LC) and robotic surgery. BACKGROUND: The assessment and development of disruptive procedures often follow a chaotic and unstructured approach. The IDEAL paradigm has offered a sequential 5-stage process to assess controversial surgical strategies like ALPPS, which was introduced in 2012 to expand liver surgery for primarily nonresectable disease. RESULTS: By October 2024, the international ALPPS registry collected 1349 cases from 146 centers in 46 countries. Early reports unveiled an alarming morbidity and perioperative mortality. Accumulating cases in the registry and a consensus conference enabled to reduce the initial 90-day mortality rates >15% to <5% in high-volume centers. Meta-analyses, long-term follow-up and a RCT were available through the growing data in the registry. In comparison, the development of LC was similarly marked by technical advances and a registry to highlight safety (especially bile duct injuries). A small multicenter RCT (and a larger one later) supported an unstoppable wave of rapid adoption by patients and surgeons. Robotic surgery is currently going through close scrutinization by many stakeholders in view of the massive promotion by the industry, but a compelling registry is still missing. CONCLUSIONS: ALPPS has now reached a high-level of evaluation with clear guidelines for use thanks to international collaborations and the IDEAL paradigm. This may serve as template for future evaluations of surgical innovations.
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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 | 0.029 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".