Training curriculum in minimally invasive emergency digestive surgery: 2022 WSES position paper
Bibliographic record
Abstract
BACKGROUND: Minimally invasive surgery (MIS), including laparoscopic and robotic approaches, is widely adopted in elective digestive surgery, but selectively used for surgical emergencies. The present position paper summarizes the available evidence concerning the learning curve to achieve proficiency in emergency MIS and provides five expert opinion statements, which may form the basis for developing standardized curricula and training programs in emergency MIS. METHODS: This position paper was conducted according to the World Society of Emergency Surgery methodology. A steering committee and an international expert panel were involved in the critical appraisal of the literature and the development of the consensus statements. RESULTS: Thirteen studies regarding the learning curve in emergency MIS were selected. All but one study considered laparoscopic appendectomy. Only one study reported on emergency robotic surgery. In most of the studies, proficiency was achieved after an average of 30 procedures (range: 20-107) depending on the initial surgeon's experience. High heterogeneity was noted in the way the learning curve was assessed. The experts claim that further studies investigating learning curve processes in emergency MIS are needed. The emergency surgeon curriculum should include a progressive and adequate training based on simulation, supervised clinical practice (proctoring), and surgical fellowships. The results should be evaluated by adopting a credentialing system to ensure quality standards. Surgical proficiency should be maintained with a minimum caseload and constantly evaluated. Moreover, the training process should involve the entire surgical team to facilitate the surgeon's proficiency. CONCLUSIONS: Limited evidence exists concerning the learning process in laparoscopic and robotic emergency surgery. The proposed statements should be seen as a preliminary guide for the surgical community while stressing the need for further research.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".