2020 WSES guidelines for the detection and management of bile duct injury during cholecystectomy
Bibliographic record
Abstract
Abstract Bile duct injury (BDI) is a dangerous complication of cholecystectomy, with significant postoperative sequelae for the patient in terms of morbidity, mortality, and long-term quality of life. BDIs have an estimated incidence of 0.4–1.5%, but considering the number of cholecystectomies performed worldwide, mostly by laparoscopy, surgeons must be prepared to manage this surgical challenge. Most BDIs are recognized either during the procedure or in the immediate postoperative period. However, some BDIs may be discovered later during the postoperative period, and this may translate to delayed or inappropriate treatments. Providing a specific diagnosis and a precise description of the BDI will expedite the decision-making process and increase the chance of treatment success. Subsequently, the choice and timing of the appropriate reconstructive strategy have a critical role in long-term prognosis. Currently, a wide spectrum of multidisciplinary interventions with different degrees of invasiveness is indicated for BDI management. These World Society of Emergency Surgery (WSES) guidelines have been produced following an exhaustive review of the current literature and an international expert panel discussion with the aim of providing evidence-based recommendations to facilitate and standardize the detection and management of BDIs during cholecystectomy. In particular, the 2020 WSES guidelines cover the following key aspects: (1) strategies to minimize the risk of BDI during cholecystectomy; (2) BDI rates in general surgery units and review of surgical practice; (3) how to classify, stage, and report BDI once detected; (4) how to manage an intraoperatively detected BDI; (5) indications for antibiotic treatment; (6) indications for clinical, biochemical, and imaging investigations for suspected BDI; and (7) how to manage a postoperatively detected BDI.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".