Background: The use of machine learning (ML) and artificial intelligence (AI) in medical research continues to grow as the amount and availability of clinical data expands. These techniques allow complex interpretation of data and capture non-linear relations not immediately apparent by classic statistical techniques. Methods: This review of the ML/AI literature provides a brief overview for practicing surgeons and clinicians of the current and future roles these methods will have within surgical infection research. Results: A conceptual overview of the techniques is provided along with concrete examples in the surgical infections literature. Further examples of ML/AI techniques in clinical decision support as well as therapy discovery with model-based deep reinforcement learning are illustrated. Conclusions: Artificial intelligence and ML are important and increasingly utilized techniques within the expanding body of surgical infection research. This article provides a minimal baseline literacy in ML/AI to be able to view such projects in an appropriately critical fashion.

Objectives Damage control laparotomy (DCL) remains an important tool in the trauma surgeon’s armamentarium. Inconsistency in reporting standards have hindered careful scrutiny of DCL outcomes. We sought to develop a core outcome set (COS) for DCL clinical studies to facilitate future pooling of data via meta-analysis and Bayesian statistics while minimizing reporting bias. Methods A modified Delphi study was performed using DCL content experts identified through Eastern Association for the Surgery of Trauma (EAST) ‘landmark’ DCL papers and EAST ad hoc COS task force consensus. Results Of 28 content experts identified, 20 (71%) participated in round 1, 20/20 (100%) in round 2, and 19/20 (95%) in round 3. Round 1 identified 36 potential COS. Round 2 achieved consensus on 10 core outcomes: mortality, 30-day mortality, fascial closure, days to fascial closure, abdominal complications, major complications requiring reoperation or unplanned re-exploration following closure, gastrointestinal anastomotic leak, secondary intra-abdominal sepsis (including anastomotic leak), enterocutaneous fistula, and 12-month functional outcome. Despite feedback provided between rounds, round 3 achieved no further consensus. Conclusions Through an electronic survey-based consensus method, content experts agreed on a core outcome set for damage control laparotomy, which is recommended for future trials in DCL clinical research. Further work is necessary to delineate specific tools and methods for measuring specific outcomes. Level of evidence V, criteria