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Background: Two-stage breast reconstruction is a common technique used to restore preoperative appearance in patients undergoing mastectomy. However, capsular contracture may develop and lead to implant failure and significant morbidity. The objective of this study is to build a machine-learning model that can determine the risk of developing contracture formation after two-stage breast reconstruction. Methods: A total of 209 women (406 samples) were included in the study cohort. Patient characteristics that were readily accessible at the preoperative visit and details pertaining to the surgical approach were used as input data for the machine-learning model. Supervised learning models were assessed using 5-fold cross validation. A neural network model is also evaluated using a 0.8/0.1/0.1 train/validate/test split. Results: Among the subjects, 144 (35.47%) developed capsular contracture. Older age, smaller nipple-inframammary fold distance, retropectoral implant placement, synthetic mesh usage, and postoperative radiation increased the odds of capsular contracture (p < 0.05). The neural network achieved the best performance metrics among the models tested, with a test accuracy of 0.82 and area under receiver operative curve of 0.79. Conclusion: To our knowledge, this is the first study that uses a neural network to predict the development of capsular contraction after two-stage implant-based reconstruction. At the preoperative visit, surgeons may counsel high-risk patients on the potential need for further revisions or guide them toward autologous reconstruction.

Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM data in Utah, it was discovered that five types of ATSPM data anomalies (data switching, data shifting, data missing under 6 months, data missing over 6 months, and irregular curves) were present in the data. To address the data issues, this paper presents a method that enables transportation agencies to automatically detect data anomalies in their ATSPM datasets. The proposed method utilizes the moving average and standard deviation of a moving window to calculate the z-score for traffic volume data points at each timestamp. Anomalies are flagged when the z-score exceeds 2, which is based on the data falling within two standard deviations of the mean. The results demonstrate that this method effectively identifies anomalies within ATSPM systems, thereby enhancing the usability of data for engineers, planners, and all ATSPM users. By employing this method, transportation agencies can improve the efficiency of their ATSPM systems, leading to more accurate and reliable data for analysis.

Summary:. Two-stage implant-based reconstruction after mastectomy may require secondary revision procedures to treat complications, correct defects, and improve aesthetic outcomes. Patients should be counseled on the possibility of additional procedures during the initial visit, but the likelihood of requiring another procedure is dependent on many patient- and surgeon-specific factors. This study aims to identify patient-specific factors and surgical techniques associated with higher rates of secondary procedures and offer a machine learning model to compute individualized assessments for preoperative counseling. A training set of 209 patients (406 breasts) who underwent two-stage alloplastic reconstruction was created, with 45.57% of breasts (185 of 406) requiring revisional or unplanned surgery. On multivariate analysis, hypertension, no tobacco use, and textured expander use corresponded to lower odds of additional surgery. In contrast, higher initial tissue expander volume, vertical radial incision, and larger nipple-inframammary fold distance conferred higher odds of additional surgery. The neural network model trained on clinically significant variables achieved the highest collective performance metrics, with ROC AUC of 0.74, sensitivity of 84.2, specificity of 63.6, and accuracy of 62.1. The proposed machine learning model trained on a single surgeon’s data offers a precise and reliable tool to assess an individual patient’s risk of secondary procedures. Machine learning models enable physicians to tailor surgical planning and empower patients to make informed decisions aligned with their lifestyle and preferences. The utilization of this technology is especially applicable to plastic surgery, where outcomes are subject to a variety of patient-specific factors and surgeon practices, including threshold to perform secondary procedures.

Background Breast anesthesia is commonly reported after mastectomy and reconstruction. During deep inferior epigastric perforator (DIEP) flap reconstruction, we coapt at least one of the T10 to T12 thoracoabdominal nerves within the flap to the anterior cutaneous branch of the third intercostal nerve using a nerve allograft. We aim to evaluate the efficacy of nerve grafting in improving sensory recovery following neurotized DIEP flap reconstruction.

Models to predict ramp meter queue length from traffic detector data are potentially useful tools in improving traffic operations and safety. Existing research, however, has been based on microscopic simulation or relied on extensive calibration of Kalman filter and related models to produce reliable queue length estimates. This research seeks to develop methodologies for improving and simplifying the calibration process of existing queue length models by applying loop detector data including volume, occupancy, and the metering rate data for ramp meters along I-15 in Utah. A conservation model and several variations of a Kalman filter model generated estimated queues that were compared to observed queue lengths in 60 s bins. A modified Kalman filter model and a new heuristic model derived from cluster analysis—the models that yielded the best results—provided queue length estimates that were generally within approximately eight vehicles of the observed queue length. Using the ramp metering rate, the queue length estimates were converted into wait times that were generally within approximately 30 s of the actual wait time, producing a viable method to predict wait time from up-to-the-minute traffic detection information with relatively little required calibration. The implementation of the ramp meter queue length and wait time estimation algorithms presented in this research will allow departments of transportation to better assess freeway and ramp conditions, which can then aid in reducing congestion throughout the freeway network.

Summary:. Our team recently described targeted nipple reinnervation (TNR) during female-to-male gender-affirming mastectomy with free nipple grafting using either direct nerve coaptation or nerve allograft. The goals of TNR are to improve sensation (including erogenous sensation) and prevent numbness, paresthesias, chronic pain, and phantom sensation. Here, we describe our modified technique, which has evolved to use autologous intercostal nerve branches as donor nerves for reinnervation if direct nerve coaptation cannot be achieved. During TNR, the T3-T5 sensory branches are preserved and coapted to the repositioned nipple–areolar complex (NAC). In patients with donor nerves that were not adequate in length to allow for direct coaptation, autologous intercostal nerve branches were not used for coaptation (branches present along the chest wall that would otherwise be lost) or one of the T3-T5 branches were harvested. An end-to-end nerve repair between the autograft and donor nerves was done, and the donor nerve/autograft complex was coapted to the NAC. Targeted muscle reinnervation was performed after autograft harvest to prevent neuroma formation. TNR with intercostal nerve autograft is technically feasible in female-to-male gender-affirming mastectomy with free nipple grafting when direct coaptation is not possible. Chest reinnervation using autologous intercostal nerve branches as donor nerves is another option for reinnervation when the nerves are too short for direct coaptation. Because the collection of long-term data is ongoing, the effectiveness of NAC reinnervation using our technique will be described in a future publication.

Automated traffic signal performance measures (ATSPMs) are used to collect data concerning the current and historical performance of signalized intersections. However, transportation agencies are not using ATSPM data to the full extent of this “big data” resource, because the volume of information can overwhelm traditional identification and prioritization techniques. This paper presents a method that summarizes multiple dimensions of intersection- and corridor-level performance using ATSPM data and returns information that can be used for prioritization of intersections and corridors for further analysis. The method was developed and applied to analyze three signalized corridors in Utah, consisting of 20 total intersections. Four performance measures were used to develop threshold values for evaluation: platoon ratio, split failures, arrivals on green, and red-light violations. The performance measures were scaled and classified using k-means cluster analysis and expert input. The results of this analysis produced a score for each intersection and corridor determined from the average of the four measures, weighted by expert input. The methodology is presented as a prototype that can be developed with more performance measures and more extensive corridors for future studies.

Abstract Background Current animal tracking studies are most often based on the application of external geolocators such as GPS and radio transmitters. While these technologies provide detailed movement data, they are costly to acquire and maintain, which often restricts sample sizes. Furthermore, deploying external geolocators requires physically capturing and recapturing of animals, which poses an additional welfare concern. Natural biomarkers provide an alternative, non-invasive approach for addressing a range of geolocation questions and can, because of relatively low cost, be collected from many individuals thereby broadening the scope for population-wide inference. Methods We developed a low-cost, minimally invasive method for distinguishing between local versus non-local movements of cattle using sulfur isotope ratios (δ34S) in cattle tail hair collected in the Greater Serengeti Ecosystem, Tanzania. Results We used a Generalized Additive Model to generate a predicted δ34S isoscape across the study area. This isoscape was constructed using spatial smoothers and underpinned by the positive relationship between δ34S values and lithology. We then established a strong relationship between δ34S from recent sections of cattle tail hair and the δ34S from grasses sampled in the immediate vicinity of an individual’s location, suggesting δ34S in the hair reflects the δ34S in the environment. By combining uncertainty in estimation of the isoscape, with predictions of tail hair δ34S given an animal’s position in the isoscape we estimated the anisotropic distribution of travel distances across the Serengeti ecosystem sufficient to detect movement using sulfur stable isotopes. Conclusions While the focus of our study was on cattle, this approach can be modified to understand movements in other mobile organisms where the sulfur isoscape is sufficiently heterogeneous relative to the spatial scale of animal movements and where tracking with traditional methods is difficult.

We report the results of an experiment to evaluate the impact of transit signal priority (TSP) on headway adherence for a bus rapid transit (BRT) system in Provo / Orem, Utah. The bus requests TSP based on its unpublished schedule, but users perceive only a headway. Quantile regression models estimated on raw timepoint data from the BRT system reveal that TSP significantly improves headway adherence, after controlling for peak times, direction, and cumulative trip dwell time. We also find that requiring the bus to be 2 minutes late before requesting TSP improves headway adherence more than allowing all buses to request TSP.

One common challenge of modeling intersection related crash data is the high proportion of sites with zero crashes. Extensive research has been done on appropriate methods to handle excess zeroes. There is some reluctance to use zero-inflated models in the traffic safety literature. The primary purpose of this paper is to evaluate zero-inflated models to determine if they are a suitable method for modeling crash counts. An appropriate approach to model selection is to choose the model that best accomplishes research objectives rather than attempting to discover the true underlying data generating process. Thus using zero-inflated models is warranted when they outperform other models relative to research objectives. In addition, using zero-inflated models does not assume sites are in an inherently safe or unsafe state, and should not be summarily dismissed on the basis of disagreement with the hypothesized underlying data generating process. Secondarily, we compare implementations of zero-inflated Poisson, zero-inflated negative binomial, and negative binomial-Lindley Bayesian hierarchical models using intersection related crash data for the state of Utah from 2014 to 2018. We specifically compare the quality of fit as determined by a Bayesian χ2 test for goodness-of-fit and their relative predictive accuracy. The zero-inflated negative binomial performs best overall. We conclude that there are cases where zero-inflated models perform as well or better than other comparable models and may be considered as a viable option to model crash counts.

Over the past few years, the Utah Department of Transportation has developed the signal performance metrics (SPMs) system to evaluate the performance of signalized intersections dynamically. This system currently provides data summaries for several performance measures, one of them being turning movement counts collected by microwave sensors. As this system became public, there was a need to evaluate the accuracy of the data placed on the SPMs. A large-scale data collection was carried out to meet this need. Vehicles in the Hi-resolution data from microwave sensors were matched with the vehicles by ground-truth volume count data. Matching vehicles from the microwave sensor data and the ground-truth data manually collected required significant effort. A spreadsheet-based data analysis procedure was developed to carry out the task. A mixed model analysis of variance was used to analyze the effects of the factors considered on turning volume count accuracy. The analysis found that approach volume level and number of approach lanes would have significant effect on the accuracy of turning volume counts but the location of the sensors did not significantly affect the accuracy of turning volume counts. In addition, it was found that the location of lanes in relation to the sensor did not significantly affect the accuracy of lane-by-lane volume counts. This indicated that accuracy analysis could be performed by using total approach volumes without comparing specific turning counts, that is, left-turn, through and right-turn movements. In general, the accuracy of approach volume counts collected by microwave sensors were within the margin of error that traffic engineers could accept. The procedure taken to perform the analysis and a summary of accuracy of volume counts for the factor combinations considered are presented in this paper.

BackgroundGymnastics requires intense year-round upper and lower extremity strength training typically starting from an early age. As such, the injury patterns observed in these athletes may be unique.PurposeTo characterize the types of injuries and provide return-to-sport data in male and female collegiate gymnasts.Study designDescriptive epidemiology study.MethodsA conference-specific injury database was utilized to perform a retrospective review of injuries for male and female National Collegiate Athletic Association (NCAA) Division I gymnasts within the Pacific Coast Conference between 2017 and 2020 (N = 673 gymnasts). Injuries were stratified by anatomic location, sex, time missed, and injury diagnoses. Relative risk (RR) was used to compare results between sexes.ResultsOf the 673 gymnasts, 183 (27.2%) experienced 1093 injuries during the study period. Injuries were sustained in 35 of 145 male athletes (24.1%) as compared with 148 of 528 female athletes (28.0%; RR, 0.86 [95% CI, 0.63-1.19]; P = .390). Approximately 66.1% (723/1093) of injuries occurred in a practice setting, compared with 84 of 1093 injuries (7.7%) occurring during competition. Overall, 417 of 1093 injuries (38.2%) resulted in no missed time. Shoulder injuries and elbow/arm injuries were significantly more common in male versus female athletes (RR, 1.99 [95% CI, 1.32-3.01], P = .001; and RR, 2.08 [95% CI, 1.05-4.13], P = .036, respectively). In total, 23 concussions affected 21 of 673 athletes (3.1%); 6 concussions (26.1%) resulted in the inability to return to sport during the same season.ConclusionFor the majority of musculoskeletal injuries, the gymnasts were able to return to sport during the same season. Male athletes were more likely to experience shoulder and elbow/arm injuries, likely because of sex-specific events. Concussions occurred in 3.1% of the gymnasts, highlighting the need for vigilant monitoring. This analysis of the incidence and outcomes of injuries observed in NCAA Division I gymnasts may guide injury prevention protocols as well as provide important prognostic information.

BackgroundThere are few data on injuries suffered by collegiate water polo athletes.PurposeTo describe the epidemiology of injuries suffered by National College Athletic Association (NCAA) male and female water polo players by using injury surveillance data over a 5-year period.Study designDescriptive epidemiology study.MethodsDeidentified data on all water polo injuries and illnesses recorded in the Pac-12 Sports Injury Research Archive from July 2016 through June 2021 were obtained and analyzed. Three men's and 4 women's teams were observed for the entire 5-year period, and 1 men's and 1 women's team was observed from July 2018 through June 2021.ResultsDuring the observation period, 729 injuries were recorded in the database, with no differences in overall injury rates between male and female athletes (relative risk [RR] = 1.0; 95% CI, 0.9-1.2); 33.7% of injuries required a physician encounter, and 3.6% required surgery. The shoulder was the most injured body part, making up 20.6% of all injuries, followed by the head/face (18.8%) and hand/wrist/forearm (11.7%). Shoulder tendinopathy was the most common shoulder injury diagnosis (4.5% of all injuries). Concussion was the most common injury diagnosis overall, making up 11.4% of injuries, and 81.9% of concussions occurred outside of competition. Male athletes were significantly more likely than female athletes to have a concussion in an off-season practice (RR, 3.25; 95% CI, 1.2-8.8) and via contact with another player (RR, 2.9; 95% CI, 1.3-6.4). Half of the 26 surgical procedures occurring over the observation period were for chronic joint trauma of the groin/hip/pelvis/buttock, with 9 of those 13 being for femoroacetabular impingement specifically.ConclusionAmong NCAA water polo athletes, the shoulder was the most injured body part; however, shoulder injuries rarely required missed time from sport or necessitated surgical intervention. Concussions were the most common injury diagnosis, had the worst return-to-play outcomes among common diagnoses, and were mostly sustained outside of competition. Femoroacetabular impingement was found to be the dominant diagnosis for which surgical intervention was required.

BackgroundThere is limited literature characterizing the incidence, variety, and effects of injuries and illnesses observed in elite swimmers.PurposeTo describe the epidemiology of injuries and illnesses affecting elite intercollegiate competitive swimmers.Study designDescriptive epidemiology study.MethodsThis retrospective study utilized a deidentified injury and illness database of National Collegiate Athletic Association Division I swimmers in the Pacific Coast Conference from the academic years 2016 to 2017 and 2019 to 2020. A health event was defined as an illness or musculoskeletal injury that was identified by an athletic trainer or team physician. Musculoskeletal injuries and nonmusculoskeletal injuries and illnesses were stratified by body location. Injuries were further characterized as career-ending, season-ending, missed time but the athlete returned to sport in the same season, or those that did not cause missed time. Relative risk (RR) was used to compare the percentage of athletes affected between women and men, with statistical significance being defined by a 95% CI not including 1.ResultsIncluded were 641 collegiate swimmers (301 male, 340 female). There were 1030 health events among 277 women and 173 men, with 635 (61.7%) occurring in women and 395 (38.3%) in men. There were 540 musculoskeletal injuries reported, most of which involved the shoulder (n = 126; 23.3%), spine (n = 95; 17.6%), foot/ankle/lower leg (n = 81; 15.0%), knee/thigh (n = 67; 12.4%), and hand/wrist/forearm (n = 52; 9.6%). A total of 490 nonmusculoskeletal health events were reported and included events such as respiratory tract infections (n = 119; 24.3%), unspecified medical illness (n = 93; 19.0%), concussions (n = 58; 11.8%), ear infections (n = 25; 5.1%), and gastrointestinal illnesses (n = 24; 4.9%). Compared with male swimmers, female swimmers were at a higher risk of sustaining both musculoskeletal injury (RR, 1.5; 95% CI, 1.22-1.83) and nonmusculoskeletal injury/illness (RR, 1.32; 95% CI, 1.04-1.68). There were 58 documented concussions, with 8 (13.8%) being season-ending, but not career-ending and 14 (24.1%) being career-ending. Women had a higher rate of concussion (9.1% vs 4.3% for men; RR, 2.11; 95% CI, 1.13-3.96).ConclusionThis retrospective study identified the most common injuries and illnesses observed among elite collegiate swimmers. Awareness of the incidence and outcome of injuries and illnesses that affect competitive swimmers may allow for more targeted analyses and injury prevention strategies.