ABSTRACT
BACKGROUND AND OBJECTIVES: Giant pituitary adenomas (GPAs) are a challenging clinical entity, composing 5% to 15% of all pituitary adenomas. While the endoscopic endonasal transsphenoidal (EET) approach has surpassed the microsurgical transsphenoidal (MT) and transcranial (TC) approaches as the first-line surgical modality in most institutions, a systematic review comparing the 3 approaches has not been undertaken since 2012. Given growing adoption of EET and development of novel operative techniques over the past decade, an updated comparison of GPA surgical modalities is warranted. METHODS: We identified all studies related to the surgical management of GPAs in PubMed, Embase, and Web of Science from inception to December 31, 2021. End points assessed included gross total resection (GTR) rates, postoperative visual improvement, mortality, and perioperative complications. RESULTS: After screening of 1701 studies, we identified 45 studies on the surgical management of GPAs for meta-analysis. Thirty-one used the EET approach (n = 1413), 11 studies used the MT approach (n = 601), and 10 used the TC approach (n = 416). The cumulative number of patients treated by EET did not exceed that of patients treated by the TC or MT approaches until 2014 and 2015, respectively. Despite patients undergoing EET having the highest average tumor diameter, pooled rates for GTR were significantly higher for EET (42%) than MT (33%, P < .001) and TC (8%, P < .001) and EET similarly exhibited superior rates of visual improvement (85%) than MT (73%, P < .001) and TC (56%, P < .001). Mortality rates were comparable between EET (0.6%) and MT (1.6%), but EET had significantly lower mortality than TC (2.7%, P < .001). Compared with MT, EET had lower rates of hypopituitarism (8.5% vs 14.9%, P = .012) but higher rates of diabetes insipidus (3.1% vs 0.5%, P = .001). CONCLUSION: In an updated meta-analysis of 1413 patients with GPA, EET resection conferred significantly higher rates of visual improvement and GTR, when compared with the MT and TC approaches.
ABSTRACT
OBJECTIVE: Artificial intelligence (AI)-based text generators such as Chat Generative Pre-Trained Transformer (ChatGPT) have come into the forefront of modern medicine. Given the similarity between AI-generated and human-composed text, tools need to be developed to quickly differentiate the two. Previous work has shown that simple grammatical analysis can reliably differentiate AI-generated text from human-written text. STUDY DESIGN: In this study, ChatGPT was used to generate 25 articles related to obstetric topics similar to those made by the American College of Obstetrics and Gynecology (ACOG). All articles were geared towards patient education. These AI-generated articles were then analyzed for their readability and grammar using validated scoring systems and compared to real articles from ACOG. RESULTS: Characteristics of the 25 AI-generated articles included fewer overall characters than original articles (mean 3,066 vs. 7,426; p < 0.0001), a greater average word length (mean 5.3 vs. 4.8; p < 0.0001), and a lower Flesch-Kincaid score (mean 46 vs. 59; p < 0.0001). With this knowledge, a new scoring system was develop to score articles based on their Flesch-Kincaid readability score, number of total characters, and average word length. This novel scoring system was tested on 17 new AI-generated articles related to obstetrics and 7 articles from ACOG, and was able to differentiate between AI-generated articles and human-written articles with a sensitivity of 94.1% and specificity of 100% (Area Under the Curve [AUC] 0.99). CONCLUSION: As ChatGPT is more widely integrated into medicine, it will be important for health care stakeholders to have tools to separate originally written documents from those generated by AI. While more robust analyses may be required to determine the authenticity of articles written by complex AI technology in the future, simple grammatical analysis can accurately characterize current AI-generated texts with a high degree of sensitivity and specificity. KEY POINTS: · More tools are needed to identify AI-generated text in obstetrics, for both doctors and patients.. · Grammatical analysis is quick and easily done.. · Grammatical analysis is a feasible and accurate way to identify AI-generated text..
ABSTRACT
OBJECTIVE: The aim of this study was to evaluate the efficacy of transcarotid arterial revascularization (TCAR) as a viable intervention in the treatment of symptomatic carotid artery stenosis. METHODS: The authors performed a retrospective review of prospectively collected data of the first 62 consecutive patients treated at Rhode Island Hospital in Providence, Rhode Island, who underwent a TCAR for symptomatic carotid artery stenosis between November 11, 2020, and March 31, 2022. Relevant demographic, comorbidity, and perioperative data were extracted through retrospective chart review. Patients with asymptomatic carotid artery stenosis were excluded. The authors also evaluated patients using pertinent physiological and anatomical high-risk criteria as described in the ROADSTER trial. Risk factors were aggregated to form a composite risk total for every patient. The primary outcome of this study was the 30-day adverse outcome rate of stroke, myocardial infarction, and/or death. Periprocedural stroke was identified by clinical symptoms and radiographic findings. Secondary endpoints included device and procedural success, 30-day mortality, 30-day stroke rate, and postoperative complications. RESULTS: The authors analyzed the first 62 patients with > 50% symptomatic carotid artery stenosis who underwent TCAR at their institution. The mean age of the cohort was 71.5 years, and the cohort was predominantly male (67.7%). The most common high-risk medical criteria were age older than 75 years (45.3%) and severe coronary artery disease (13.6%). The most common anatomical high-risk criteria were high bifurcation (35.1%) and contralateral stenosis requiring treatment within 30 days (15.8%). Fifty percent of patients had at least 1 medical high-risk criterion, 50% had at least 1 anatomical risk criterion, and 82% of patients had 2 or more high-risk criteria of any kind. Among this group, all patients (100%) underwent successful revascularization, with 1 (1.6%) requiring intraprocedural conversion to carotid endarterectomy. Postprocedurally, there was 1 nondisabling stroke (1.6%) and 3 deaths (4.8%) within 30 days of the procedure, with only 1 death directly attributable to the procedure. One patient (1.6%) experienced a neck hematoma. In total, 4 patients (6.5%) experienced a major complication. The overall complication rate was 8.0%. CONCLUSIONS: The authors' initial experience with TCAR suggests that it might provide an effective alternative to carotid endarterectomy and carotid artery stenting in the management of symptomatic carotid stenosis in patients with high-risk anatomical and medical characteristics.
