Prehospital whole blood transfusion improves probability of survival over transfusion within one hour of arrival to a trauma center.

Background: Hemorrhagic shock remains the leading cause of mortality in trauma patients and prehospital whole blood transfusion (PHWBT) improves outcomes in patients with significant shock burden. This study evaluates the impact of PHWBT on the probability of survival (PS) in trauma patients.

Methods: We compared PS determined via the Trauma and Injury Severity Score (TRISS) equation, for patients receiving PHWB and WB within 1 ​h of arrival to our Level 1 academic trauma center (EDWB).

Selective Nonoperative Management of a Pediatric Abdominal Gunshot Wound.

The standard of care for penetrating abdominal trauma (PAT) has traditionally been exploratory laparotomy. However, significant rates of surgical morbidity and nontherapeutic laparotomies have prompted the development of alternative strategies. Selective nonoperative management (SNOM) is one such approach, which can be considered for hemodynamically stable patients without signs of peritonitis.

From shortages to solutions: Liquid plasma as a practical alternative to whole blood for prehospital trauma resuscitation.

Trauma-induced hemorrhagic shock remains a leading cause of preventable mortality, necessitating timely and effective resuscitation strategies. While low-titer O whole blood (LTOWB) is the preferred choice for prehospital resuscitation due to its balanced composition and ease of use, overall widespread implementation is hindered by persistent supply chain issues and daily logistical challenges of access and deployment. Platelets, containing plasma as a component, are considered the next best alternative to LTOWB but are constrained by their short shelf life and ongoing scarcity, and ongoing storage compliance, rendering their use impractical.

An In-Depth Analysis of the State Laws That License International Medical Graduates Without American Residency Training.

International medical graduates (IMGs) have been integral to the United States (US) healthcare system and have helped tackle physician shortages for over a century. Current data suggest that by 2030, almost half the states will suffer from physician shortage and estimate a deficit of almost 139,000 physician jobs nationally. These numbers raise concern and call for innovative strategies to mitigate the potential problem.

Surgical Outcomes of Vertical Rectus Abdominis Myocutaneous Flap Pelvic Reconstruction.

Background: The vertical rectus abdominis myocutaneous (VRAM) flap has emerged as the workhorse flap for perineal and pelvic reconstruction. The authors aimed to evaluate outcomes of the VRAM flap over a 20-year period and the role of mesh abdominal wall reinforcement following VRAM flap-based reconstruction.

Methods: The authors conducted a retrospective review of all consecutive patients who underwent pelvic reconstruction with a VRAM flap between January of 2001 and March of 2021.

Quality of videos on thrombolysis and thrombectomy for deep venous thrombosis.

Objective: has gained popularity as an unofficial educational resource for surgical trainees, but its content's quality and educational value remain to be evaluated. The aim of this study is to analyze the current content on these techniques for lower extremity DVT (LEDVT) on .

Methods: A search was performed on using 13 search terms in August 2022 on a clear-cached browser.

Evaluation of modified frailty index for predicting post-operative outcomes after lower-extremity free-flap reconstruction.

Background: Risk stratification for complex procedures such as microsurgical reconstruction of the lower extremities is an important part of preoperative planning and counseling. The aim of this study was to determine the effectiveness of the modified five-item frailty index (5-mFI) score, a validated tool for assessing risk in surgical patients, in predicting postoperative complications after lower extremity (LE) free flap reconstruction.

Methods: A retrospective review of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was conducted from 2010 to 2020 on patients who underwent LE free-flap reconstruction.

Did the COVID-19 Pandemic Impact the Caliber of Trainees Taken During Match 2021? A Survey of General Surgery Residency Program Directors.

Background: The cancellation of clinical rotations (CRs) and implementation of virtual interviews (VIs) profoundly affected the residency selection process leading up to the 2021 NRMP Match. The authors investigated how these changes influenced the caliber of applicants taken by general surgery (GS) residency programs from the perspectives of program directors (PDs).

Methods: A 14 question, web-based electronic survey was emailed to PDs of ACGME-accredited GS residency programs.

Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery.

Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations.

New Frontiers of Natural Language Processing in Surgery.

The vast and ever-growing volume of electronic health records (EHR) have generated a wealth of information-rich data. Traditional, non-machine learning data extraction techniques are error-prone and laborious, hindering the analytical potential of these massive data sources. Equipped with natural language processing (NLP) tools, surgeons are better able to automate, and customize their review to investigate and implement surgical solutions.

Artificial Intelligence and Machine Learning in Surgery.

In the present era, the technology of artificial intelligence has started to rapidly gain popularity as a revolutionary innovation in healthcare. The following article serves as the introduction to our symposium on artificial intelligence in surgery.

Obesity Stratification Predicts Short-Term Complications After Parastomal Hernia Repair.

Introduction: While previous studies have documented adverse outcomes among obese patients undergoing ventral and inguinal hernia repairs, there is a lack of literature regarding the impact of obesity on parastomal hernia (PSH) repair. This retrospective study aims to determine the value of obesity stratification in predicting postoperative complications in patients undergoing PSH repair.

Materials And Methods: Outcomes of elective PSH repairs from 2010 to 2020 in the American College of Surgeons National Surgical Quality Improvement Program database were analyzed.

Impact of the Virtual Format on Plastic Surgery Residency and Fellowship Interviews: A National Cross-Sectional Study.

Background: The 2020 to 2021 residency and fellowship application cycles were profoundly affected by the introduction of virtual interviews. The authors investigated the impact the virtual format had on plastic surgery residency and fellowship interviews from the perspectives of program directors.

Methods: Surveys were sent to program directors of integrated plastic surgery residency and fellowship programs to ascertain their perspectives regarding the virtual format's impact on residency and fellowship interviews.

Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning.

Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs.

A Synopsis of Artificial Intelligence and its Applications in Surgery.

Artificial intelligence (AI) has made steady in-roads into the healthcare scenario over the last decade. While widespread adoption into clinical practice remains elusive, the outreach of this discipline has progressed beyond the physician scientist, and different facets of this technology have been incorporated into the care of surgical patients. New AI applications are developing at rapid pace, and it is imperative that the general surgeon be aware of the broad utility of AI as applicable in his or her day-to-day practice, so that healthcare continues to remain up-to-date and evidence based.

Interviewing Amidst a Pandemic: Perspectives of US Residency Program Directors on the Virtual Format.

COVID-19 imposed significant limitations upon the 2021 U.S. National Resident Matching Program (NRMP), most important of which is the replacement of traditional in-person interviews with a virtual format.

A Surgeon's Guide to Artificial Intelligence-Driven Predictive Models.

Artificial intelligence (AI) focuses on processing and interpreting complex information as well as identifying relationships and patterns among complex data. Artificial intelligence- and machine learning (ML)-driven predictions have shown promising potential in influencing real-time decisions and improving surgical outcomes by facilitating screening, diagnosis, risk assessment, preoperative planning, and shared decision-making. Fundamental understanding of the algorithms, as well as their development and interpretation, is essential for the evolution of AI in surgery.

Advancing Surgical Education: The Use of Artificial Intelligence in Surgical Training.

The technology of artificial intelligence (AI) has made significant in-roads into the field of medicine over the last decade. With surgery being a discipline where repetition is the key to mastery, the scope of AI presents enormous potential for resident education through the analysis of technique and delivery of structured feedback for performance improvement. In an era marred by a raging pandemic that has decreased exposure and opportunity, AI offers an attractive solution towards improving operating room efficiency, safe patient care in the hands of supervised residents and can ultimately culminate in reduced health care costs.

Deep Learning Applications in Surgery: Current Uses and Future Directions.

Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data.

Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications.

Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively.