Notable surgical trials in gynecologic oncology: a 10-year overview.

In the last decade, we have witnessed important advances in novel therapeutics in the management of gynecologic cancers. These studies have built on the findings from preexisting data and have provided incremental contributions leading to changes that have not only impacted the accuracy of cancer detection and its metastatic components but also led to improvements in oncologic outcomes and quality of life. Key landmark trials have changed the standard of care in cervix, uterine, and ovarian cancer. A number of these have been controversial and have generated significant debate among gynecologic oncologists. The main objective of this review was to provide an overview on each of these trials as a reference for immediate and consolidated access to the study aims, methodology, results, and conclusion.

Surgical site infection prevention bundle in gynecology oncology surgery: a key element in the implementation of an enhanced recovery after surgery (ERAS) program.

Surgical site infection rates are among 5-35% in all gynecologic oncology procedures. Such infections lead to increased patient morbidity, reduction in quality of life, higher likelihood of readmissions, and reinterventions, which contribute directly to mortality and increase in health-related costs. Some of these are potentially preventable by applying evidence-based strategies in the peri-operative patient setting. The objective of this review is to provide recommendations for the individual components that most commonly comprise the surgical site infection prevention bundles that could be implemented in gynecologic oncology procedures. We searched articles from relevant publications with specific topics related to each surgical site infection intervention chosen to be reviewed. Studies on each topic were selected with an emphasis on meta-analyses, systematic reviews, randomized control studies, non-randomized controlled studies, reviews, clinical practice guidelines, and case series. Data synthesis was done through content and thematic analysis to identify key themes in the included studies. This review intends to serve as the most up-to-date frame of evidence-based peri-operative care in our specialty and could serve as the first initiative to introduce an enhanced recovery after surgery (ERAS) program.

Association of reviewer experience with discriminating human-written versus ChatGPT-written abstracts.

OBJECTIVE: To determine if reviewer experience impacts the ability to discriminate between human-written and ChatGPT-written abstracts. METHODS: Thirty reviewers (10 seniors, 10 juniors, and 10 residents) were asked to differentiate between 10 ChatGPT-written and 10 human-written (fabricated) abstracts. For the study, 10 gynecologic oncology abstracts were fabricated by the authors. For each human-written abstract we generated a ChatGPT matching abstract by using the same title and the fabricated results of each of the human generated abstracts. A web-based questionnaire was used to gather demographic data and to record the reviewers' evaluation of the 20 abstracts. Comparative statistics and multivariable regression were used to identify factors associated with a higher correct identification rate. RESULTS: The 30 reviewers discriminated 20 abstracts, giving a total of 600 abstract evaluations. The reviewers were able to correctly identify 300/600 (50%) of the abstracts: 139/300 (46.3%) of the ChatGPT-generated abstracts and 161/300 (53.7%) of the human-written abstracts (p=0.07). Human-written abstracts had a higher rate of correct identification (median (IQR) 56.7% (49.2-64.1%) vs 45.0% (43.2-48.3%), p=0.023). Senior reviewers had a higher correct identification rate (60%) than junior reviewers and residents (45% each; p=0.043 and p=0.002, respectively). In a linear regression model including the experience level of the reviewers, familiarity with artificial intelligence (AI) and the country in which the majority of medical training was achieved (English speaking vs non-English speaking), the experience of the reviewer (ß=10.2 (95% CI 1.8 to 18.7)) and familiarity with AI (ß=7.78 (95% CI 0.6 to 15.0)) were independently associated with the correct identification rate (p=0.019 and p=0.035, respectively). In a correlation analysis the number of publications by the reviewer was positively correlated with the correct identification rate (r28)=0.61, p<0.001. CONCLUSION: A total of 46.3% of abstracts written by ChatGPT were detected by reviewers. The correct identification rate increased with reviewer and publication experience.