The rapid emergence of publicly accessible artificial intelligence platforms such as large language models (LLMs) has led to an equally rapid increase in articles exploring their potential benefits and risks. We performed a bibliometric analysis of ChatGPT literature in medicine and science to better understand publication trends and knowledge gaps. Following title, abstract, and keyword searches of PubMed, Embase, Scopus, and Web of Science databases for ChatGPT articles published in the medical field, articles were screened for inclusion and exclusion criteria. Data were extracted from included articles, with citation counts obtained from PubMed and journal metrics obtained from Clarivate Journal Citation Reports. After screening, 267 articles were included in the study, most of which were editorials or correspondence with an average of 7.5 +/− 18.4 citations per publication. Published articles on ChatGPT were authored largely in the United States, India, and China. The topics discussed included use and accuracy of ChatGPT in research, medical education, and patient counseling. Among non-surgical specialties, radiology published the most ChatGPT-related articles, while plastic surgery published the most articles among surgical specialties. The average citation number among the top 20 most-cited articles was 60.1 +/− 35.3. Among journals with the most ChatGPT-related publications, there were on average 10 +/− 3.7 publications. Our results suggest that managing the inevitable ethical and safety issues that arise with the implementation of LLMs will require further research exploring the capabilities and accuracy of ChatGPT, to generate policies guiding the adoption of artificial intelligence in medicine and science.

(1) Introduction: Traumatic brain injury (TBI) is a leading cause of injury and mortality worldwide, carrying an estimated cost of $38 billion in the United States alone. Neutrophil to lymphocyte ratio (NLR) has been investigated as a standardized biomarker that can be used to predict outcomes of TBI. The aim of this review was to determine the prognostic utility of NLR among patients admitted for TBI. (2) Methods: A literature search was conducted in PubMed, Scopus, and Web of Science in November 2022 to retrieve articles regarding the use of neutrophil to lymphocyte ratio (NLR) as a prognostic measure in traumatic brain injury (TBI) patients. Inclusion criteria included studies reporting outcomes of TBI patients with associated NLR values. Exclusion criteria were studies reporting only non-primary data, those insufficiently disaggregated to extract NLR data, and non-English or cadaveric studies. The Newcastle-Ottawa Scale was utilized to assess for the presence of bias in included studies. (3) Results: Following the final study selection 19 articles were included for quantitative and qualitative analysis. The average age was 46.25 years. Of the 7750 patients, 73% were male. Average GCS at presentation was 10.51. There was no significant difference in the NLR between surgical vs. non-surgical cohorts (SMD 2.41 95% CI −1.82 to 6.63, p = 0.264). There was no significant difference in the NLR between bleeding vs. non-bleeding cohorts (SMD 4.84 95% CI −0.26 to 9.93, p = 0.0627). There was a significant increase in the NLR between favorable vs. non-favorable cohorts (SMD 1.31 95% CI 0.33 to 2.29, p = 0.0090). (4) Conclusions: Our study found that NLR was only significantly predictive for adverse outcomes in TBI patients and not surgical treatment or intracranial hemorrhage, making it nonetheless an affordable alternative for physicians to assess patient prognosis.

With a growing number of elderly patients requiring elective and non-elective procedures, frailty-based preoperative risk stratification is an emerging tool in orthopedic surgery to minimize adverse postoperative outcomes. This paper sought to understand the current literature regarding preoperative Orthopedic Frailty Risk Stratification (OFRS) and describe the disparate frailty indices and their capabilities for discrimination in predicting adverse postoperative outcomes. A literature search was conducted in Pubmed, Cochrane, and Scopus for articles published during or prior to February 2024 assessing frailty following surgery for orthopedic pathologies. Qualitative variables including study characteristics and application of frailty were collected and synthesized. Quantitative meta-analysis was performed for pooled odds ratio (OR) and area under the curve (AUC) of frailty for mortality and complications. All methods were performed in accordance with PRISMA guidelines. Of the 81 included articles, over half (52%) addressed traumatic orthopedic pathologies with traumatic hip fractures being the most studied in the OFRS (25 studies). Less common categories included oncology, sports, and foot/ankle. Functional status and independence were the most common frailty domain (25, 96.2%) and component across scales (20, 76.9%), respectively. The 5-Item Modified Frailty Index (mFI-5) was the most common frailty index (28 publications). Meta-analysis demonstrated increasing frailty was an independent predictor of mortality (30-day OR: 2.89, 95% CI: 2.00–4.18; 1 year OR: 1.81, 95% CI: 1.48–2.22, p < 0.001), major complications (OR: 1.63, 95% CI: 1.10–2.41, p = 0.02), and Clavien-Dindo IV complications (OR: 3.26, 95% CI: 2.18–4.87, p < 0.001). Frailty had good discriminatory accuracy for predicting mortality at 30-days (AUC: 0.71, 95% CI: 0.68–0.74, p < 0.001), 3-months (OR: 0.75, 95% CI: 0.65–0.83, p < 0.001), and 1-year (OR:0.74, 95% CI: 0.73–0.75, p < 0.001). The orthopedic surgery frailty literature is extremely heterogeneous, with disparate frailty scales implemented to measure varying outcomes across many orthopedic pathologies. Despite no consensus on exact scales or definitions, various frailty indices have predicted adverse outcomes.