Diversity and Expertise Among Authors of U.S. Surgical Society Clinical Guidelines.

BackgroundClinical practice guidelines (CPGs) shape surgical care and outcomes, but concerns persist regarding the diversity and expertise of their authors. Whether U.S.

National trends in crowdfunding for neurosurgery: Scalable natural language processing of publicly available data using a large language model.

Objective: Online crowdfunding, commonly used to cover healthcare costs for vulnerable populations, is directly linked to health disparities and gaps in social safety-net systems. The nationwide impact of crowdfunding on neurosurgery remains unclear. We aimed to characterize the funds raised, success rate, geographic distribution, and most frequent conditions for neurosurgery-related crowdfunding campaigns.

Common statistical pitfalls in clinical research: The guide to avoid misinterpreting p values, confidence intervals, odds ratios, and more.

Most clinical research relies on frequentist statistics-a framework that, while widely used, is not intuitive and susceptible to profound misinterpretation. Common misinterpretations arise when frequentist concepts such as confidence intervals are interpreted through the more-intuitive Bayesian lens, leading to false conclusions and misinformation that can spread unchecked. Even among researchers with formal statistics training, frequentist statistics demands deliberate study and review to avoid navigate prevalent analytic pitfalls.

Artificial intelligence in surgery research: Successfully implementing AI clinical decision support models.

Artificial intelligence (AI) in surgery literature typically encompasses decision support models that aim to help clinicians make better decisions. Many studies report developing and validating models, yet few models are implemented at the bedside. Exceedingly few models achieve their intended goal upon implementation.

Regression analysis in clinical research.

Abstract: Regression modeling is a vital tool that develops correlations and associations between exposure and outcome. The outcome's characteristics and how it is captured in the data ultimately guides the decision to which model is selected. However, the interpretation and the statistics that go into model selection and study design dictate the validity of the model.

Prospective multicenter external validation of the rib fracture frailty index.

Background: The Rib Fracture Frailty (RFF) Index is an internally validated machine learning-based risk assessment tool for adult patients with rib fractures that requires minimal provider entry. Existing frailty risk scores have yet to undergo head-to-head performance comparison with age, a widely used proxy for frailty in clinical practice. Our aim was to externally validate the RFF Index in a small-scale implementation feasibility study.

CT pan-scanning versus targeted imaging among older adults after ground level falls.

Background: Ground level falls (GLFs) among older adults are responsible for millions of injuries. Routine pan-scanning (CT imaging of the head, chest, abdomen and pelvis) is commonly employed; however, we lack robust evidence of its benefit. We investigated whether pan-scanning identifies a larger proportion of patients with significant injury or injury requiring procedural intervention and hypothesized resuscitation area findings-including radiographs, ultrasound, and external signs of injury-would detect these injuries.

An introduction to clinical prediction models using logistic regression in acute care surgery research: Methodologic considerations and common pitfalls.

Clinical prediction models can enhance timely clinical decision-making when appropriately developed and integrated within clinical workflows. A risk prediction model is typically a regression equation that uses patient risk factor data to estimate the probability of the presence of disease (diagnostic) or its future occurrence (prognostic). Risk prediction models are widely studied in the surgical literature and commonly developed using logistic regression.

Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study.

Background And Aims: Emerging evidence supports artificial intelligence-enhanced electrocardiogram (AI-ECG) for detecting acute myocardial infarction (AMI), but real-world validation is needed. The aim of this study was to evaluate the performance of AI-ECG in detecting AMI in the emergency department (ED).

Methods: The Rule-Out acute Myocardial Infarction using Artificial intelligence Electrocardiogram analysis (ROMIAE) study is a prospective cohort study conducted in the Republic of Korea from March 2022 to October 2023, involving 18 university-level teaching hospitals.

Time delay and evidence profiles forming clinical recommendations of US surgical society guidelines.

Introduction: Surgical society guidelines facilitate implementation of up-to-date, evidence-based care, but concerns regarding the contemporality and quality of evidence can hinder adherence. We aimed to evaluate the time gap between evidence publication and their inclusion within clinical guidelines-the publication-to-guideline delay-and characterize the quality of evidence within contemporary surgical society guidelines.

Study Design: This cross-sectional study analyzed guidelines published by U.

Beyond American College of Surgeons Verification: Quality Metrics Associated with High Performance at Level I and II Trauma Centers.

Background: The American College of Surgeons (ACS) Committee on Trauma has established a framework for trauma center quality improvement. Despite efforts, recent studies show persistent variation in patient outcomes across national trauma centers. We aimed to investigate whether risk-adjusted mortality varies at the hospital level and if high-performing centers demonstrate better adherence to ACS Verification, Review, and Consultation (VRC) program quality measures.

Practical guide to building machine learning-based clinical prediction models using imbalanced datasets.

Clinical prediction models often aim to predict rare, high-risk events, but building such models requires robust understanding of imbalance datasets and their unique study design considerations. This practical guide highlights foundational prediction model principles for surgeon-data scientists and readers who encounter clinical prediction models, from feature engineering and algorithm selection strategies to model evaluation and design techniques specific to imbalanced datasets. We walk through a clinical example using readable code to highlight important considerations and common pitfalls in developing machine learning-based prediction models.

Serratus anterior plane block improves pain and incentive spirometry volumes in trauma patients with multiple rib fractures: a prospective cohort study.

Background: Rib fractures are common injuries associated with considerable morbidity, long-term disability, and mortality. Early, adequate analgesia is important to mitigate complications such as pneumonia and respiratory failure. Regional anesthesia has been proposed for rib fracture pain control due to its superior side effect profile compared with systemic analgesia.

The burden of readmissions after rib fractures among older adults.

Background: The index hospitalization morbidity and mortality of rib fractures among older adults (aged ≥65 years) is well-known, yet the burden and risks for readmissions after rib fractures in this vulnerable population remain understudied. We aimed to characterize the burdens and etiologies associated with 3-month readmissions among older adults who suffer rib fractures. We hypothesized that readmissions would be common and associated with modifiable etiologies.

Career Trajectory After General Surgery Residency: Do Academic Program Graduates Pursue Academic Surgery?

Objective: To determine the proportion of contemporary U.S. academic general surgery residency program graduates who pursue academic careers and identify factors associated with pursuing academic careers.

Thoracic and Lumbar Spine Injury: Evidence-Based Diagnosis, Management, and Outcomes.

Background: Traumatic thoracolumbar spine injuries are associated with significant morbidity and mortality. Targeted for non-spine specialist trauma surgeons, this systematic scoping review aimed to examine literature for up-to-date evidence on presentation, management, and outcomes of thoracolumbar spine injuries in adult trauma patients.

Methods: This review was reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist.

Development and Validation of a Model to Quantify Injury Severity in Real Time.

Importance: Quantifying injury severity is integral to trauma care benchmarking, decision-making, and research, yet the most prevalent metric to quantify injury severity-Injury Severity Score (ISS)- is impractical to use in real time.

Objective: To develop and validate a practical model that uses a limited number of injury patterns to quantify injury severity in real time through 3 intuitive outcomes.

Design, Setting, And Participants: In this cohort study for prediction model development and validation, training, development, and internal validation cohorts comprised 223 545, 74 514, and 74 514 admission encounters, respectively, of adults (age ≥18 years) with a primary diagnosis of traumatic injury hospitalized more than 2 days (2017-2018 National Inpatient Sample).

TraumaICD Bidirectional Encoder Representation From Transformers: A Natural Language Processing Algorithm to Extract Injury International Classification of Diseases, 10th Edition Diagnosis Code From Free Text.

Objective: To develop and validate TraumaICDBERT, a natural language processing algorithm to predict injury International Classification of Diseases, 10th edition (ICD-10) diagnosis codes from trauma tertiary survey notes.

Background: The adoption of ICD-10 diagnosis codes in clinical settings for injury prediction is hindered by the lack of real-time availability. Existing natural language processing algorithms have limitations in accurately predicting injury ICD-10 diagnosis codes.