Jabal, Mohamed Sobhi

Background: The goal of this study was to characterize the references provided as supporting evidence of the Plastic Surgery In-service Training Examination (PSITE) syllabi, including those on the novel “core surgical principles” section. Methods: We analyzed the references from five consecutive PSITE Examination syllabi (2016–2020). We collected the following information from each question: question section, total number of references, and source of publication of each reference. Results: We analyzed 1250 questions and 3436 references. Plastic and Reconstruction Surgery was overall the most frequently referenced journal followed by Journal of Hand Surgery (American Volume) and Annals of Plastic Surgery. The most commonly referenced textbooks were Plastic Surgery (by Neligan), Green’s Operative Hand Surgery, and Grabb and Smith’s Plastic Surgery. Regarding the “core surgical principles” section, Plastic and Reconstruction Surgery remained the most frequently cited journal, followed by the Journal of the American Medical Association, New England Journal of Medicine, Annals, and Aesthetic Surgery Journal. “Core surgical principles” contained the largest number of unique journals (n = 209) among all test sections. Within the “core” section, Statistics in Medicine was the most frequently referenced textbook followed by Grabb and Smith’s Plastic Surgery. Conclusions: The main plastic surgery texts and literature were used to support approximately half of the answers within the “core surgical principles” section. The references within this section originated from the largest number of different journals, thus highlighting the breadth and variety of this content and the challenges in preparing for this section.

AbstractPurposeIdentifying predictive factors for all‐cause reoperation after anterior cruciate ligament reconstruction could inform clinical decision making and improve risk mitigation. The primary purposes of this study are to (1) determine the incidence of all‐cause reoperation after anterior cruciate ligament reconstruction, (2) identify predictors of reoperation after anterior cruciate ligament reconstruction using machine learning methodology, and (3) compare the predictive capacity of the machine learning methods to that of traditional logistic regression.MethodsA longitudinal geographical database was utilized to identify patients with a diagnosis of new anterior cruciate ligament injury. Eight machine learning models were appraised on their ability to predict all‐cause reoperation after anterior cruciate ligament reconstruction. Model performance was evaluated via area under the receiver operating characteristics curve. To explore modeling interpretability and radiomic feature influence on the predictions, we utilized a game‐theory‐based method through SHapley Additive exPlanations.ResultsA total of 1400 patients underwent anterior cruciate ligament reconstruction with a mean postoperative follow‐up of 9 years. Two‐hundred and eighteen (16%) patients experienced a reoperation after anterior cruciate ligament reconstruction, of which 6% of these were revision ACL reconstruction. SHapley Additive exPlanations plots identified the following risk factors as predictive for all‐cause reoperation: diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. XGBoost was the best‐performing model (area under the receiver operating characteristics curve of 0.77) and outperformed logistic regression in this regard.ConclusionsAll‐cause reoperation after anterior cruciate ligament reconstruction occurred at a rate of 16%. Machine learning models outperformed traditional statistics and identified diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair as predictive risk factors for reoperation. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. These models will allow surgeons to tabulate individualized risk for future reoperation for patients undergoing anterior cruciate ligament reconstruction.Level of evidenceIII.

Introduction Various clinical trials addressing large core acute ischemic stroke (AIS) are ongoing from multiple international groups. Future development of clinical guidelines depends on meta‐analyses of these trials calling for a degree of homogeneity of elements across the different studies. The aim of this common data element study was to provide an overview of the key features of pertinent large core infarct trials. Methods PubMed and ClinicalTrials.gov databases were screened for published and ongoing clinical trials assessing mechanical thrombectomy in patients with AIS with large core infarct. Nested Knowledge AutoLit living review platform was utilized to categorize primary and secondary outcomes as well as inclusion and exclusion criteria for patient selection in the clinical trials. Results The most reported data element was ASPECTS score, but with varied definitions of what compromises large core. Non‐utility‐weighted modified Rankin Score (mRS) was reported in 6/7 studies as the primary outcome, while the utility‐weighted mRS was the outcome of interest in the TESLA trial, all of them at the 3‐months mark, with only LASTE looking for mRS shift at the 6‐months mark. Secondary outcomes had more variations. Mortality is reported separately only in 4/7 trials, all at the 3‐month mark. Additionally, the TENSION trial reports the frequency of serious adverse events, including mortality, at the 1‐week and 12‐month mark. Conclusions In the published and the ongoing large core trials, there is a large degree of variability in the collected data elements. Differences in definition and timepoints renders reaching a unified standard difficult, which hinders high quality meta‐analyses and cohesive evidence‐driven synthesis.

Background Federal research funding is highly sought after but may be challenging to attain. A clear understanding of funding for specific diseases, such as cerebrovascular disorders, might help researchers regarding which National Institutes of Health (NIH) institutes fund research into specific disorders and grant types. Objective To examine the current scope of NIH grant funding for cerebrovascular conditions. Methods The NIH-developed RePORTER was used to extract active NIH-funded studies related to cerebrovascular diseases through January 2023. Duplicate studies were removed, and projects were manually screened and labeled in subcategories as clinical and basic science and as research subcategories. Extracted data included total funding, grant types, institutions that received funding, and diseases studied. Python (version 3.9) and SciPy library were used for statistical analyses. Results We identified 1232 cerebrovascular projects across seven diseases with US$699 952 926 in total funding. The cerebrovascular diseases with the greatest number of grants were ischemic stroke (705, or 57.2% of all funded projects), carotid disease (193, or 15.7%), and hemorrhagic stroke (163, or 13.2%). R01 grants were the most common mechanism of funding (632 grants, or 51.3%). The National Institute of Neurological Disorders and Stroke (NINDS) funded the most projects (504 projects; US$325 536 405), followed by the National Heart, Lung, and Blood Institute (NHLBI) (376 projects; US$216 784 546). Conclusion Cerebrovascular disease receives roughly US$700 million in NIH funding. Ischemic stroke accounts for the majority of NIH-funded cerebrovascular projects, and R01 grants are the most common funding mechanism. Notably, NHLBI provides a large proportion of funding, in addition to NINDS.

Background Computed tomography (CT) angiography collateral score (CTA-CS) is an important clinical outcome predictor following mechanical thrombectomy for ischemic stroke with large vessel occlusion (LVO). The present multireader study aimed to evaluate the performance of e-CTA software for automated assistance in CTA-CS scoring. Materials and Methods Brain CTA images of 56 patients with anterior LVO were retrospectively processed. Twelve readers of various clinical training, including junior neuroradiologists, senior neuroradiologists, and neurologists graded collateral flow using visual CTA-CS scale in two sessions separated by a washout period. Reference standard was the consensus of three expert readers. Duration of reading time, inter-rater reliability, and statistical comparison of readers’ performance metrics were analyzed between the e-CTA assisted and unassisted sessions. Results e-CTA assistance resulted in significant increase in mean accuracy (58.6% to 67.5%, p = 0.003), mean F1 score (0.574 to 0.676, p = 0.002), mean precision (58.8% to 68%, p = 0.007), and mean recall (58.7% to 69.9%, p = 0.002), especially with slight filling deficit (CTA-CS 2 and 3). Mean reading time was reduced across all readers (103.4 to 59.7 s, p = 0.001), and inter-rater agreement in CTA-CS assessment was increased (Krippendorff's alpha 0.366 to 0.676). Optimized occlusion laterality detection was also noted with mean accuracy (92.9% to 96.8%, p = 0.009). Conclusion Automated assistance for CTA-CS using e-CTA software provided helpful decision support for readers in terms of improving scoring accuracy and reading efficiency for physicians with a range of experience and training backgrounds and leading to significant improvements in inter-rater agreement.

Background Flow diverters have been widely used in clinical practice for more than a decade. However, most outcome data are limited to 1 year timepoints. This study aims to offer meta-analysis data on long-term (>1 year) safety and effectiveness results for patients with aneurysms treated with flow diverters. Methods PubMed, Web of Science, Embase, and SCOPUS were searched up to February 24, 2022 using the AutoLit platform. We included primary studies assessing the long-term outcomes for flow diverter devices to manage unruptured internal carotid artery aneurysms with a follow-up period of >1 year. The meta-analysis was carried out using Comprehensive Meta-Analysis software (CMA). Results Eleven studies were included in the meta-analysis. The pooled occlusion rates after flow diversion treatment for unruptured intracranial brain aneurysms were 77%, 87.4%, 84.5%, 89.4%, 96% for 1 year, 1–2 years, 2 years, 3 years, and 5 years follow-up, respectively. The in-stent stenosis rate was 4.8% and the retreatment rate for the long-term follow-up period was 5%. No delayed rupture of the aneurysm was reported, and there was one case of delayed ischemic stroke. The sensitivity analysis of the prospective studies showed a complete occlusion rate of 83.5% and 85.2% for 1 and 3 years of follow-up, respectively. Conclusion Flow diverters are safe and effective in short- and long-term follow-up and rarely cause serious delayed side effects.