Journal of Robotic Surgery最新文献

Background: Despite the rapid rise of short-video platforms as health information sources, the informational quality of robotic-surgery videos-an area requiring precise clinical explanation-remains insufficiently studied.

Objective: To compare the quality and reliability of robotic-surgery videos on TikTok and Bilibili and determine how platform features and uploader identity shape content quality.

Methods: We conducted a cross-sectional analysis of 200 videos (100 per platform) scored using the Global Quality Score (GQS) and modified DISCERN (mDISCERN). Videos were coded by uploader type and theme, and group differences and predictors were examined with nonparametric tests and multivariable regression.

Results: TikTok videos drew more engagement, but Bilibili posts were longer and scored higher on GQS and mDISCERN. Professional uploaders produced the most reliable content. Engagement metrics correlated with each other yet did not predict quality in regression.

Conclusions: On short-video platforms, visibility and scientific accuracy diverge-highlighting the need for professional involvement, quality-sensitive governance, and stronger public digital health literacy.

Excessive central airway collapse (ECAC), encompassing tracheobronchomalacia (TBM) and excessive dynamic airway collapse (EDAC), is increasingly recognized as a debilitating condition with substantial symptomatic and functional burden. Robotic-assisted tracheobronchoplasty (R-TBP) has emerged as a minimally invasive alternative to open repair; however, its collective clinical outcomes have not been systematically synthesized. We conducted a systematic review of comparative and non-comparative studies reporting outcomes of R-TBP, following PRISMA guidelines (PROSPERO: CRD420251241127). A systematic search of PubMed, Scopus, Cochrane, and Web of Science was performed through August 14, 2025. Outcomes included postoperative pulmonary function, functional exercise capacity (6MWT), patient-reported outcome measures (PROMs), perioperative parameters, and complications. Due to substantial heterogeneity in outcome definitions, follow-up intervals, and reporting standards, a quantitative meta-analysis was precluded, and a narrative synthesis was performed. Risk of bias was assessed using ROBINS-I. Four studies comprising 231 patients were included. Operative duration ranged from 264 to 504 min. Postoperative recovery was rapid, characterized by minimal ICU utilization (0 to 1.33 days) and a median hospital length of stay of 2.33 to 5 days markedly shorter than historical open benchmarks. Clinical efficacy was most pronounced in patient-reported outcomes: studies consistently reported substantial quality-of-life improvements, including SGRQ reductions of 11.6 to 36.7 points. Functional capacity also showed improvement, with 6MWT distance increases ranging from 10.3 to 76.3 m. Conversely, pulmonary function outcomes were heterogeneous; while some cohorts showed modest gains in predicted FEV1 (+ 1.7% to + 8%), others demonstrated stabilization rather than significant improvement. Safety profiles were acceptable, with major complications (Clavien-Dindo ≥ IIIa) ranging from 14% to 21%. R-TBP represents a technically demanding yet increasingly viable alternative to open surgery. The available data consistently demonstrates clear benefits regarding postoperative recovery and patient-reported quality of life, suggesting that symptomatic relief outweighs the inconsistent spirometric improvements often seen in this population. Current evidence positions R-TBP as a safe approach with superior immediate recovery metrics compared to historical open standards. While the certainty of evidence is limited by retrospective designs, the consistency of the safety signal supports R-TBP's role in the surgical armamentarium. Future comparative trials should prioritize patient-reported outcomes and functional measures like the 6MWT over static spirometry to accurately gauge efficacy.

Obesity remains a major public health challenge in Italy, and robotic-assisted metabolic bariatric surgery (MBS) is gaining increasing attention for its potential advantages in precision and recovery. However, data on short-term outcomes from Italian centres are still limited. This study aimed to evaluate six-month postoperative outcomes of robotic gastric bypass in Italian adults, focusing on weight and BMI reduction, and exploring subgroup differences by gender and comorbidity status. This retrospective observational study was conducted at a primary care hospital in Italy and included 60 consecutive adult patients who underwent robotic gastric bypass with complete six-month follow-up. Demographic data, surgical details, and anthropometric measures (weight and BMI) were collected at baseline, two months, and six months postoperatively. Subgroup analyses were performed according to gender and comorbidity status. Repeated-measures ANOVA and appropriate comparative analyses were used to assess statistical significance. The cohort was predominantly female (68.33%), with a mean age of 48.2 ± 10.3 years. The mean baseline weight and BMI were 128.4 ± 29.7 kg and 45.9 ± 9.2 kg/m², respectively. At six months, patients achieved a mean weight loss of 40.6 kg (31.6% total weight loss [%TWL]) and a BMI reduction of 14.3 kg/m² (P < 0.00001 for all comparisons across time points). No major complications were reported, with a minor complication rate of 5.0%. Male patients exhibited slightly higher mean reductions in weight and BMI and longer operative times, but these differences were not statistically significant. Patients with comorbidities generally experienced greater improvements in anthropometric outcomes compared to those without. Robotic gastric bypass in a primary care hospital setting is associated with substantial short-term reductions in weight and BMI, with particularly pronounced benefits in patients with comorbid conditions. These findings support the feasibility and efficacy of robotic metabolic bariatric surgery in the Italian healthcare context, though longer follow-up is needed to assess durability of outcomes.

Urology has been a leading field in the adoption of robotic surgery, which offers technical advantages and low complication rates, including notably reduced intraoperative blood loss. In this study, we aimed to examine the relationship between formula-based estimated blood loss and visually estimated intraoperative blood loss in robotic urologic procedures. In this retrospective study, 111 robot-assisted urologic surgery were included. The agreement between the visually estimated intraoperative blood loss and the estimated values calculated using different formulas (Hb dilution method formula, Hb mass method, Gross Formula, López-Picado Formula). To determine how consistent each estimation was with the visually estimated intraoperative blood loss and with each other, Bland-Altman analysis, Concordance Correlation Coefficient (CCC) and Intraclass Correlation Coefficient (ICC) were applied. Intraoperative blood loss (visual estimation) indicated a mean blood loss of 220.72 ± 212.61 mL, whereas formula-based calculations consistently yielded higher estimates: López-Picado, 721.64 ± 532 mL; Hb mass method, 667.79 ± 429 mL; Gross formula, 726.97 ± 540 mL; and Hb dilution method, 737.99 ± 545 mL. The analyses revealed that all formulas differed statistically significantly from the visually estimated intraoperative blood loss. Evaluation of agreement and consistency demonstrated that the formulas showed poor agreement both with estimated blood loss and with one another. The strongest concordance was observed between López-Picado and Gross formula. There was a large discrepancy between visually estimated intraoperative blood loss and formula-based estimations. While formula-based methods show strong internal consistency, they differ substantially from the subjective estimates commonly used.

The transition from laparoscopic to robotic surgery for left-sided colorectal cancer raises safety concerns during the learning curve, particularly when complex cases are preferentially selected for the robotic platform. We evaluated a machine learning-based framework for risk-adjusted safety monitoring of robotic implementation, using outcomes from an established laparoscopic program as the reference. We retrospectively analyzed adult patients who underwent minimally invasive left-sided colorectal resection for malignancy between May 2023 and September 2025. A penalized logistic regression model predicting a composite adverse endpoint (anastomotic leak, reoperation, major complication, unplanned intensive care admission, or mortality) was developed in a laparoscopic training cohort (n = 211) using four preoperative variables (age, body mass index, American Society of Anesthesiologists physical status, and tumor location). Model-derived expected risks were applied to a robotic cohort (n = 93) to construct a risk-adjusted cumulative sum (RA-CUSUM) chart. The robotic cohort included a higher proportion of rectal tumors and more frequent neoadjuvant therapy than the laparoscopic cohort and had longer operative times, whereas the composite adverse event rate was similar (12.9% vs. 13.3%). The RA-CUSUM curve for the robotic series fluctuated around the expected risk baseline derived from the laparoscopic benchmark without a sustained upward drift. These findings suggest that, in this single-center experience, early robotic adoption did not show a clear signal of excess risk-adjusted short-term adverse events despite increased case complexity and demonstrate the feasibility of embedding a laparoscopic-derived risk model into RA-CUSUM analysis as a pragmatic tool for learning curve assessment.