No abstract

Background Although self‐expandable metal stent (SEMS) placement as bridge to surgery (BTS) in patients with left‐sided obstructing colonic cancer has shown promising short‐term results, it is used infrequently owing to uncertainty about its oncological safety. This population study compared long‐term oncological outcomes between emergency resection and SEMS placement as BTS. Methods Through a national collaborative research project, long‐term outcome data were collected for all patients who underwent resection for left‐sided obstructing colonic cancer between 2009 and 2016 in 75 Dutch hospitals. Patients were identified from the Dutch Colorectal Audit database. SEMS as BTS was compared with emergency resection in the curative setting after 1 : 2 propensity score matching. Results Some 222 patients who had a stent placed were matched to 444 who underwent emergency resection. The overall SEMS‐related perforation rate was 7·7 per cent (17 of 222). Three‐year locoregional recurrence rates after SEMS insertion and emergency resection were 11·4 and 13·6 per cent (P = 0·457), disease‐free survival rates were 58·8 and 52·6 per cent (P = 0·175), and overall survival rates were 74·0 and 68·3 per cent (P = 0·231), respectively. SEMS placement resulted in significantly fewer permanent stomas (23·9 versus 45·3 per cent; P < 0·001), especially in elderly patients (29·0 versus 57·9 per cent; P < 0·001). For patients in the SEMS group with or without perforation, 3‐year locoregional recurrence rates were 18 and 11·0 per cent (P = 0·432), disease‐free survival rates were 49 and 59·6 per cent (P = 0·717), and overall survival rates 61 and 75·1 per cent (P = 0·529), respectively. Conclusion Overall, SEMS as BTS seems an oncologically safe alternative to emergency resection with fewer permanent stomas. Nevertheless, the risk of SEMS‐related perforation, as well as permanent stoma, might influence shared decision‐making for individual patients.

Background Machine learning (ML) has been successful in several fields of healthcare, however the use of ML within bariatric surgery seems to be limited. In this systematic review, an overview of ML applications within bariatric surgery is provided. Methods The databases PubMed, EMBASE, Cochrane, and Web of Science were searched for articles describing ML in bariatric surgery. The Cochrane risk of bias tool and the PROBAST tool were used to evaluate the methodological quality of included studies. Results The majority of applied ML algorithms predicted postoperative complications and weight loss with accuracies up to 98%. Conclusions In conclusion, ML algorithms have shown promising capabilities in the prediction of surgical outcomes after bariatric surgery. Nevertheless, the clinical introduction of ML is dependent upon the external validation of ML.

Publication of RCTs in high IF journals is associated with moderate improvement in methodological quality compared to RCTs published in lower IF journals. RCTs with adequate sample-size calculation, generation of allocation or intention-to-treat analysis were associated with publication in a high IF journal. On the other hand, reporting a statistically significant outcome and being industry funded were not independently associated with publication in a higher IF journal.