BackgroundSurgical videos are reshaping the landscape for surgical education. As this form of education has rapidly grown and become a valuable resource for experienced surgeons, residents, and students, there is great variability in the presentation of what is offered. This study aimed to assess and compare the educational quality of free flap instructional videos on public and paid platforms.MethodsFree flap videos from public (YouTube) and paid (American Society of Plastic Surgeons Education Network and Plastic and Reconstructive Surgery Journal) sources were screened independently by three reviewers. Sample size was calculated to reach 80% power. The educational quality of the videos was determined using a modified version of Laparoscopic Surgery Video Educational Guidelines (0–6 low, 7–12 medium, 13–18 high). Professionally‐made videos were identified per lighting, positioning, and video/imaging quality. Interrater reliability between the three reviewers was calculated. The educational quality of the videos was compared between public and paid sources using Mood's median test. Pearson's correlation coefficient was utilized to assess the correlation between video length and educational quality.ResultsSeventy‐six videos were included (40 public, 36 paid). The median video lengths for public and paid platforms were 9.43(IQR = 12.33) and 5.07(IQR = 6.4) min, respectively. There were 18 high, 16 medium, and 6 low‐quality public videos, versus 13 high, 21 medium, and 2 low‐quality paid videos. Four public and seven paid videos were identified as professionally made. Interrater reliability was high (α = .9). No differences in educational quality were identified between public and paid platforms. Video length was not correlated with quality (p = .15). A video library compiling public high‐quality videos was created (https://www.youtube.com/playlist?list=PL‐d5BBgQF75VWSkbvEq6mfYI‐‐9579oPK).ConclusionsPublic and paid platforms may provide similar surgical education on free tissue transfer. Therefore, whether to subscribe to a paid video platform for supplemental free flap education should be determined on an individual basis.
METHODS:The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) was queried for all patients undergoing cranioplasty between 2012-2020. Data for baseline demographics, comorbidities, operative characteristics, and postoperative outcomes were evaluated. Statistical analysis was performed on IBM SPSS Statistics ver. 28.0 (IBM Co., Armonk, NY, USA). The discriminatory thresholds of RAI-A, mFI-5, and increasing patient age on 30-day mortality were evaluated using receiver operating characteristics (ROC) curve analysis. The area under the curve (AUC) is represented as a C-statistic with a 95% confidence interval (CI). Two distinct multivariable analyses were conducted for RAI-A and mFI-5 to measure the independent relationships between frailty and outcomes. Covariates controlled for included race, BMI, primary procedure, cranial defect size, and material type. Additionally, the mFI-5 model was age-adjusted, allowing for direct comparison of effect sizes between age and frailty. We excluded age from the model for RAI-A to prevent any collinearity, since age contributes to the total RAI-A score.RESULTS: There were 2,864 included study patients with a median age of 57 years (IQR, 44-67), and a higher proportion of patients were female (57.0%), and white (68.5%). The RAI-A demonstrated better predictive ability for 30-day mortality (C-Statistic: 0.741, 95% CI: 0.678-0.804) compared to mFI-5 (C-Statistic: 0.574, 95% CI: 0.489-0.659) and increasing patient age (C-Statistic: 0.671, 95% CI: 0.610-0.732). On multivariable analyses, RAI-A demonstrated superior independent associations with mortality, NHD, CDII, and CDIII when compared to mFI-5 and increasing patient age. (p<0.05). CONCLUSIONS:The RAI-A demonstrated superior discrimination than the mFI-5 and increasing patient age in predicting poor outcomes following cranioplasty. The high rates of operative morbidity (5.0-36.5%) and mortality (0.4-3.2%) after cranioplasty highlight the importance of being able to predict which patients are at increased risk for poor cranioplasty outcomes, so that shared decision making regarding the potential risk and benefits of treatment can occur.
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