Evaluating Compliance of Randomized Controlled Trial Abstracts in Plastic Surgery Journals with CONSORT Guidelines Using GPT-4 AI.

Background: The quality of reporting in randomized controlled trials (RCTs) is crucial for accurate interpretation and synthesis of evidence. The Consolidated Standards of Reporting Trials (CONSORT) guidelines provide a standardized framework for reporting RCT abstracts. This study aimed to evaluate the adherence of RCT abstracts published in three major plastic surgery journals to the CONSORT tool guideline for reporting abstracts, utilizing Generative Pre-trained Transformer 4 artificial intelligence (GPT-4 AI) technology. Methods: Abstracts of RCTs published between 2010 and 2023 were collected. The GPT-4 AI model was utilized to assess the abstracts based on the CONSORT criteria. Descriptive statistics were used to report the compliance scores and identify areas where abstracts lacked compliance. Results: Of the initially identified 500 abstracts, a total of 371 RCT abstracts met the inclusion criteria and were analyzed. The mean CONSORT score was 10.05 (±2.22), with a median score of 10.72. Specific areas where abstracts lacked compliance included trial design (39.6%), participant details (28.8%), intervention descriptions (15.6%), randomization process (25.3%), and the number of participants analyzed (33.4%). Trial registration (18.3%) and funding information (15.1%) were also frequently missing. Conclusions: Our study's innovative use of the GPT-4 AI model for analysis demonstrated the potential of AI technology in streamlining and enhancing the evaluation of research compliance. We advocate for heightened awareness and more rigorous application of CONSORT guidelines among authors, reviewers, and journal editors. Emphasizing the role of AI technology in the evaluative process can further improve the reporting quality of future RCTs in plastic surgery, contributing to more reliable and transparent research in the field.

Comprehensive Systematic Review of Breast Implant Illness: Symptoms, Management, and Long-Term Outcomes.

BACKGROUND: Breast augmentation using silicone implants is common, with over 300,000 annual US surgeries. It was initially approved in 1962, there were safety concerns with the use of silicone implants, leading to a 1992-2006 FDA moratorium. Recently, Breast Implant Illness (BII) was described as a cluster of symptoms associated with silicone implants, with a lack of diagnostic criteria, which increased the likelihood of implant removal and a variety of treatment options. This comprehensive review aims to shed light on the extent, symptoms, management, and outcomes of BII. METHODS: Multiple electronic databases were systematically searched in May 2023 for studies on Breast Implant Illness utilizing terms related to BII and its symptoms, diagnosis, and treatment. Out of 1204 studies, 20 met the inclusion criteria and were included in the systematic review. The Newcastle-Ottawa Scale for the included cohort prospective and retrospective studies, and the tool for evaluating the methodological quality of case reports and case series were utilized during quality assessment. RESULTS: There were 20 papers on Breast Implant Illness published between 1995 and 2022, including a peak of papers published in 2020 and 2022. The majority of the studies came from the USA, with a variety of research methods, including case reports. Reports of arthralgia and fatigue were common findings. These symptoms were frequently alleviated by surgical procedures such as en bloc explantation and capsulectomy, highlighting the intricacy of the problem and the need for individualized treatment plans. CONCLUSION: The review underscores the critical need for further research into BII, advocating for a multidisciplinary strategy that prioritizes patient safety, informed decision-making, and comprehensive care. As the medical community progresses in understanding and managing BII, emphasizing evidence-based practices and patient-provider communication will be essential in addressing this complex condition effectively. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

The da Vinci robotic surgery system for the management of endometrial cancer: a single-center experience.

The aim of this study was to report our single-center experience of the da Vinci robotic surgery system in the management of endometrial cancer (EC) patients during 2019-2023. We descriptively summarized the preoperative, intraoperative, and postoperative outcomes. Moreover, we conducted subgroup analyses based on obesity (BMI ≥ 30 kg/m2) and the intraoperative use of indocyanine green dye (ICG). Overall, 100 patients were analyzed. Eighty-five patients (85%) were obese. The mean operative time and estimated blood loss (EBL) were 240.84 ± 70.08 min (range: 110-720) and 104.2 ± 80.3 ml (range: 20-500), respectively. The mean number of retrieved pelvic LNs was 3.01 ± 3.06 (range: 0-15). The use of ICG was employed in 58 (58%). Only a single patient (1%) underwent conversion to laparotomy. Besides, only three (3%) patients experienced vaginal laceration intraoperatively, respectively. The mean hospital stay was 1.34 ± 0.69 days (range: 1-5). No patient experienced postoperative complications. Most tumors had endometrioid histology (82%), grade-1 tumor (49%), and stage-1A disease (67%). At 3-year follow-up, only two patients (2%) developed recurrence. Patients who received the ICG had significantly lower operative time (225.86 vs. 261.52 min, p = 0.011), estimated blood loss (90 vs. 123.81 ml, p = 0.037), and hospital stay (1.12 vs. 1.64 days, p < 0.001) compared with patients who did not receive it. However, there was no significant difference between both groups regarding the number of retrieved pelvic LNs. Obesity had no significant impact on the rates of intraoperative complications, postoperative complications, and 3-year recurrence incidence. In conclusion, robotic surgery was technically feasible and safe. The use of ICG was statistically linked to favorable outcomes, in terms of decreased operative time, EBL, and hospital stay. Obesity did not impact the perioperative surgical outcomes.

Pediatric Burns: A Systematic Review and Meta-Analysis on Epidemiology, Gender Distribution, Risk Factors, Management, and Outcomes in Emergency Departments.

Pediatric burns pose a significant public health concern, ranking as the fifth most common nonfatal injury globally. This review consolidates data on the epidemiology, outcomes, and management of pediatric burns presenting to emergency departments. A systematic review was conducted across multiple databases, yielding 22 articles from 1992 to 2020. Utilizing the methodological index for non-randomized studies (MINORS) instrument, non-comparative studies scored from 2 to 11 with an average of 6.87, while comparative studies ranged from 12 to 16, averaging 13.67. The review included a total of 828,538 pediatric patients who were evaluated in the systematic review. Predominantly male victims ranged from 53% to 83%. The youngest victims were aged between 0 to 4 years. Burn etiology was largely attributed to scalds. A majority suffered from second-degree burns, with some studies reporting up to 89%. Limited data on total body surface area (TBSA) were documented, with only 2.5% requiring hospitalization. Common interventions included immediate resuscitation and skin grafting. Essential areas for future research are identified, including household risks, pre-treatment decisions, and the significant role of family dynamics in burn injury recovery. Pediatric burns remain a considerable concern, particularly among males and in household environments. The data underline the imperative for prevention strategies and optimized emergency care to positively influence outcomes for burn victims. Future research areas range from evaluating pre-treatment decisions to assessing community awareness regarding burn first aid.

Can a Machine Ace the Test? Assessing GPT-4.0's Precision in Plastic Surgery Board Examinations.

Background: As artificial intelligence makes rapid inroads across various fields, its value in medical education is becoming increasingly evident. This study evaluates the performance of the GPT-4.0 large language model in responding to plastic surgery board examination questions and explores its potential as a learning tool. Methods: We used a selection of 50 questions from 19 different chapters of a widely-used plastic surgery reference. Responses generated by the GPT-4.0 model were assessed based on four parameters: accuracy, clarity, completeness, and conciseness. Correlation analyses were conducted to ascertain the relationship between these parameters and the overall performance of the model. Results: GPT-4.0 showed a strong performance with high mean scores for accuracy (2.88), clarity (3.00), completeness (2.88), and conciseness (2.92) on a three-point scale. Completeness of the model's responses was significantly correlated with accuracy (P < 0.0001), whereas no significant correlation was found between accuracy and clarity or conciseness. Performance variability across different chapters indicates potential limitations of the model in dealing with certain complex topics in plastic surgery. Conclusions: The GPT-4.0 model exhibits considerable potential as an auxiliary tool for preparation for plastic surgery board examinations. Despite a few identified limitations, the generally high scores on key parameters suggest the model's ability to provide responses that are accurate, clear, complete, and concise. Future research should focus on enhancing the performance of artificial intelligence models in complex medical topics, further improving their applicability in medical education.