Learning and publishing opportunities for international joint reconstruction surgeons.
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ABSTRACT:
SUBMITTER: Hirakawa K
PROVIDER: S-EPMC5045465 | biostudies-literature | 2016 Sep
REPOSITORIES: biostudies-literature
Publications
Learning and publishing opportunities for international joint reconstruction surgeons.
Arthroplasty today 20160828 3
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Project description:BackgroundThe Hirsch index (h-index) is a measure that evaluates both research volume and quality-taking into consideration both publications and citations of a single author. No prior work has evaluated academic productivity and contributions to the literature of adult total joint replacement surgeons. This study uses h-index to benchmark the academic impact and identify characteristics associated with productivity of faculty members at joint replacement fellowships.MethodsAdult reconstruction fellowship programs were obtained via the American Association of Hip and Knee Surgeons website. Via the San Francisco match and program-specific websites, program characteristics (Accreditation Council for Graduate Medical Education approval, academic affiliation, region, number of fellows, fellow research requirement), associated faculty members, and faculty-specific characteristics (gender, academic title, formal fellowship training, years in practice) were obtained. H-index and total faculty publications served as primary outcome measures. Multivariable linear regression determined statistical significance.ResultsSixty-six adult total joint reconstruction fellowship programs were identified: 30% were Accreditation Council for Graduate Medical Education approved and 73% had an academic affiliation. At these institutions, 375 adult reconstruction surgeons were identified; 98.1% were men and 85.3% had formal arthroplasty fellowship training. Average number of publications per faculty member was 50.1 (standard deviation 76.8; range 0-588); mean h-index was 12.8 (standard deviation 13.8; range 0-67). Number of fellows, faculty academic title, years in practice, and formal fellowship training had a significant (P < .05) positive correlation with both h-index and total publications.ConclusionsThe statistical overview presented in this work can help total joint surgeons quantitatively benchmark their academic performance against that of their peers.
| S-EPMC5712019 | biostudies-literature
Project description:BackgroundFew details are known about open-access surgery journals that solicit manuscripts via E-mail. The objectives of this cross-sectional study are to compare solicitant surgery journals with established journals and to characterize the academic credentials and reasons for publication of their authorship.MethodsWe identified publishers who contacted the senior author and compared their surgery journals with 10 top-tier surgical journals and open-access medical journals. We assessed the senior authorship of articles published January 2017-March 2017 and utilized a blinded survey to determine motivations for publication.ResultsThroughout a 6-week period, 110 E-mails were received from 29 publishers distributing 113 surgery journals. Compared with established journals, these journals offered lesser publication fees, but also had lesser PubMed indexing rates and impact factors (all P < .002). Professors, division chiefs, and department chairs were the senior authors of nearly half of US-published papers and spent ≈$83,000 to publish 117 articles in journals with a median impact factor of 0.12 and a 33% PubMed indexing rate. Survey responses revealed a dichotomy as 43% and 57% of authors published in these journals with and without knowledge of their solicitant nature, respectively. The most commonly reported reasons for submission included waived publication fees (50%), invitation (38%), and difficulty publishing elsewhere (12%).ConclusionDespite their sparse PubMed indexing and low impact factors, many senior academic faculty publish in solicitant surgery journals. This study highlights the importance for the academic surgical community to be cognizant of the quality of a journal when reviewing the literature for research and evidence-based practice.
| S-EPMC6074048 | biostudies-literature
Project description:BackgroundThere is a paucity of data regarding compensation for early-career adult reconstruction surgeons. This study aims to quantify the time throughout the full episode of care for a Medicare primary total hip/knee arthroplasty and convert to per-hour pay for early-career arthroplasty surgeons at various geographic locations and practice settings. Using Center for Medicare and Medicaid Services data, this study also compares the compensation of early-career vs established total joint arthroplasty (TJA) surgeons.MethodsBetween January 2022 and January 2023, 3 early-career surgeons in 3 different locations collected prospective data on time spent in patient care during the global period following primary TJAs (pTJAs). A weighted average time spent per pTJA during global period was calculated with the 2024 work relative value unit and conversion factor to establish a per-hour rate. This rate was compared to the compensation rates of other healthcare-related fields and established TJA surgeons using Relative Value Scale Update Committee (RUC) values.ResultsA total of 334 pTJAs (148 hips and 186 knees) were performed among 3 surgeons, and per-hour rates of $87.62 and $87.70 were found, respectively. These are less than hospital/healthcare system/health insurance/med tech CEOs, lawyers, dentists, and travel nurses. Early-career TJA surgeons were found to take 7.98%-8.68% longer than RUC standard times for a TJA episode of care.ConclusionsThis study quantifies the per-hour compensation of early-career arthroplasty surgeons, who earn lower compensation rates to travel nurses and take longer than Center for Medicare and Medicaid Services RUC times for pTJAs. Given the increasing demand for pTJAs, decreasing reimbursement rates, and concern over burnout, access to quality pTJA care for patients is concerning.
| S-EPMC11350442 | biostudies-literature
Project description:BackgroundBreast cancer surgeries involving MS-TRAM/DIEP breast reconstruction has traditionally been collaborative efforts between breast surgeons and plastic surgeons. However, in our institution, this procedure is performed by dual-trained breast surgeons who are proficient in both breast surgery and MS-TRAM/DIEP breast reconstruction. This study aims to provide insights into the learning curve associated with this surgical approach.Materials and methodsWe included eligible breast cancer patients who underwent MS-TRAM/DIEP breast reconstruction by dual-trained breast surgeons between 2015 and 2020 at our institution. We present the learning curve of this surgical approach, with a focus on determining factors affecting flap harvesting time, surgery time, and ischemic time. Additionally, we assessed the surgical complication rates.ResultsA total of 147 eligible patients were enrolled in this study. Notably, after 30 cases, a statistically significant reduction of 1.7 h in surgery time and 21 min in ischemic time was achieved, signifying the attainment of a plateau in the learning curve. And the major and minor complications were comparable between the early and after 30 cases.ConclusionThis study explores the learning curve and feasibility experienced by dual-trained breast surgeons in performing MS-TRAM/DIEP breast reconstruction.Trial registrationNCT05560633.
| S-EPMC10865591 | biostudies-literature
Project description:PurposeIt is important to assess global trends in the practice of adult reconstruction orthopaedic surgery to understand how new evidence is being implemented. The International Society of Orthopaedic Centers (ISOC) is a consortium of academic orthopaedic centers whose members' practices likely reflect contemporary evidence and indicate how orthopaedic surgery residents and fellows are trained.MethodsWe administered a 65 question, electronic survey of adult reconstruction surgeons across the ISOC centers in September 2020 to assess practice patterns. Results were assessed using descriptive statistics or by modeling the underlying response distribution, and the analysis was stratified by hospital region.Results79 surgeons across 19 ISOC centers in 5 continents (Asia, Australia, Europe, North America, South America) completed the survey. Selected findings include: in total hip arthroplasty (THA), the posterolateral approach was used for 71 ± 42% of THA (mean ± standard deviation) and the direct anterior approach in 18% ± 34%. In total knee arthroplasty, posterior-stabilized (66% ± 39%) and cruciate-retaining (19 ± 33%) implants were most common. Robots were available in 56% (44 of 79) of surgeons' centers more commonly in Asia, Australia, and North America. Tranexamic acid was routinely used in arthroplasty by 99% (78 of 79) of surgeons. Eighty-six percent (68 of 79) submit data to joint or other registries. Virtual visits were used for 13% ± 16% of outpatient visits and by 82% (64 of 79) of surgeons overall.ConclusionsThese findings may be of use now for surgeons to consider the practices of their peers at high-volume academic institutions, and in the future as we track temporal trends.
| S-EPMC7821987 | biostudies-literature
Project description:PurposeTo develop and evaluate a novel method for computationally efficient reconstruction from noisy MR spectroscopic imaging (MRSI) data.MethodsThe proposed method features (a) a novel strategy that jointly learns a nonlinear low-dimensional representation of high-dimensional spectroscopic signals and a neural-network-based projector to recover the low-dimensional embeddings from noisy/limited data; (b) a formulation that integrates the forward encoding model, a regularizer exploiting the learned representation, and a complementary spatial constraint; and (c) a highly efficient algorithm enabled by the learned projector within an alternating direction method of multipliers (ADMM) framework, circumventing the computationally expensive network inversion subproblem.ResultsThe proposed method has been evaluated using simulations as well as in vivo 1$$ {}^1 $$ H and 31$$ {}^{31} $$ P MRSI data, demonstrating improved performance over state-of-the-art methods, with about 6 ×$$ \times $$ fewer averages needed than standard Fourier reconstruction for similar metabolite estimation variances and up to 100 ×$$ \times $$ reduction in processing time compared to a prior neural network constrained reconstruction method. Computational and theoretical analyses were performed to offer further insights into the effectiveness of the proposed method.ConclusionA novel method was developed for fast, high-SNR spatiospectral reconstruction from noisy MRSI data. We expect our method to be useful for enhancing the quality of MRSI or other high-dimensional spatiospectral imaging data.
| S-EPMC11604835 | biostudies-literature
Project description:Our technique for acromioclavicular joint reconstruction provides a variation on coracoclavicular ligament reconstruction to also include acromioclavicular ligament reconstruction. An oblique acromial tunnel is drilled, and the medial limb of the gracilis graft, after being crossed and passed beneath the coracoid and through the clavicle, is passed through this acromial tunnel and sutured to the trapezoid graft limb after appropriate tensioning. Tenodesis screws are not placed in the bone tunnels to avoid graft fraying, and initial forces on the graft are offloaded with braided absorbable sutures passed around the clavicle.
| S-EPMC4887285 | biostudies-literature
Project description:PurposeTo introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging.MethodsSingle-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution.ResultsOur combined ML + physics approach enabled Rinplane × multiband (MB) = 8- × 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T2 and T2 * parameter maps could be derived from an 8.3-second acquisition at 1 × 1 × 3-mm3 resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9- × 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network.ConclusionCombination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.
| S-EPMC6626584 | biostudies-literature
Project description:A recently introduced model-based deep learning (MoDL) technique successfully incorporates convolutional neural network (CNN)-based regularizers into physics-based parallel imaging reconstruction using a small number of network parameters. Wave-controlled aliasing in parallel imaging (CAIPI) is an emerging parallel imaging method that accelerates imaging acquisition by employing sinusoidal gradients in the phase- and slice/partition-encoding directions during the readout to take better advantage of 3D coil sensitivity profiles. We propose wave-encoded MoDL (wave-MoDL) combining the wave-encoding strategy with unrolled network constraints for highly accelerated 3D imaging while enforcing data consistency. We extend wave-MoDL to reconstruct multicontrast data with CAIPI sampling patterns to leverage similarity between multiple images to improve the reconstruction quality. We further exploit this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T2 preparation pulse (3D-QALAS). Wave-MoDL enables a 40 s MPRAGE acquisition at 1 mm resolution at 16-fold acceleration. For quantitative imaging, wave-MoDL permits a 1:50 min acquisition for T1, T2, and proton density mapping at 1 mm resolution at 12-fold acceleration, from which contrast-weighted images can be synthesized as well. In conclusion, wave-MoDL allows rapid MR acquisition and high-fidelity image reconstruction and may facilitate clinical and neuroscientific applications by incorporating unrolled neural networks into wave-CAIPI reconstruction.
| S-EPMC9774601 | biostudies-literature
Project description:Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level.
| S-EPMC11424948 | biostudies-literature