Comparison of major spine navigation platforms based on key performance metrics: a meta-analysis of 16,040 screws.

PURPOSE: The objective of this meta-analysis is to compare available computer-assisted navigation platforms by key performance metrics including pedicle screw placement accuracy, operative time, neurological complications, and blood loss. METHODS: A systematic review was conducted using major databases for articles comparing pedicle screw accuracy of computer-assisted navigation to conventional (freehand or fluoroscopy) controls via post-operative computed tomography. Outcome data were extracted and pooled by random-effects model for analysis. RESULTS: All navigation platforms demonstrated significant reduction in risk of breach, with Stryker demonstrating the highest accuracy compared to controls (OR 0.16 95% CI 0.06 to 0.41, P < 0.00001, I2 = 0%) followed by Medtronic. There were no significant differences in accuracy or most surgical outcome measures between platforms; however, BrainLab demonstrated significantly faster operative time compared to Medtronic by 30 min (95% CI - 63.27 to - 2.47, P = 0.03, I2 = 74%). Together, there was significantly lower risk of major breach in the navigation group compared to controls (OR 0.42, 95% CI 0.27-0.63, P < 0.0001, I2 = 56%). CONCLUSIONS: When comparing between platforms, Stryker demonstrated the highest accuracy, and Brainlab the shortest operative time, both followed by Medtronic. No significant difference was found between platforms regarding neurologic complications or blood loss. Overall, our results demonstrated a 60% reduction in risk of major breach utilizing computer-assisted navigation, coinciding with previous studies, and supporting its validity. This study is the first to directly compare available navigation platforms offering insight for further investigation and aiding in the institutional procurement of platforms. LEVEL 3 EVIDENCE: Meta-analysis of Level 3 studies.

Clinical applications of augmented reality in orthopaedic surgery: a comprehensive narrative review.

PURPOSE: The development of augmented reality (AR) technology allows orthopaedic surgeons to incorporate and visualize surgical data, assisting the execution of both routine and complex surgical operations. Uniquely, AR technology allows a surgeon to view the surgical field and superimpose peri-operative imaging, anatomical landmarks, navigation guidance, and more, all in one view without the need for conjugate gaze between multiple screens. The aim of this literature review was to introduce the fundamental requirements for an augmented reality system and to assess the current applications, outcomes, and potential limitations to this technology. METHODS: A literature search was performed using MEDLINE and Embase databases, by two independent reviewers, who then collaboratively synthesized and collated the results of the literature search into a narrative review focused on the applications of augmented reality in major orthopaedic sub-specialties. RESULTS: Current technology requires that pre-operative patient data be acquired, and AR-compatible models constructed. Intra-operatively, to produce manipulatable virtual images into the user's view in real time, four major components are required including a camera, computer image processing technology, tracking tools, and an output screen. The user is provided with a heads-up display, which is a transparent display, enabling the user to look at both their natural view and the computer-generated images. Currently, high-quality evidence for clinical implementation of AR technology in the orthopaedic surgery operating room is lacking; however, growing in vitro literature highlights a multitude of potential applications, including increasing operative accuracy, improved biomechanical angular and alignment parameters, and potentially reduced operative time. CONCLUSION: While the application of AR systems in surgery is currently in its infancy, we anticipate rapid and widespread implementation of this technology in various orthopaedic sub-specialties.

Key Stakeholder Perceptions of Standard vs. Total Cost of Ownership: A Procurement Analysis for Orthopaedic-Powered Instruments.

This study compares standard procurement methodology (SPM) with total cost of ownership (TCO) methodology for the procurement of orthopaedic-powered instruments. The authors conducted semi-structured standardized interviews with key hospital procurement stakeholders following consolidated criteria for reporting qualitative research. Of the 33 hospital procurement stakeholders interviewed, all (100%) reported that SPM would be easier to use than TCO. However, only six (18%) preferred SPM over TCO. Barriers to the adoption of TCO emerged as a theme. Creating TCO frameworks can help to simplify the process for procurement agents and facilitate its adoption in the healthcare sector.

The efficacy of primary sacroiliac joint fusion for low back pain caused by sacroiliac joint pathology: a systematic review and meta-analysis.

PURPOSE: Conduct a systematic review to quantify the effect of primary sacroiliac joint fusion (SIJF) for the treatment of sacroiliac (SI) joint pathology on patient reported outcomes. METHODS: Medline, Embase, Cochrane, PubMed, and Scopus databases were searched prior to August 18th, 2020 for all English-Language studies involving the treatment of SIJ pathology through SIJF and/or conservative management (CM). The quality of included studies was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Primary outcome measure was the Visual Analogue Scale (VAS) for low back pain. Secondary outcome measure was the Oswestry Disability Index (ODI) and the incidence of adverse reactions. RESULTS: A total of 564 patients and six studies were included. The overall quality of evidence analyzed by this review was low (GRADE = 0). Five out of the six studies were industry funded. The VAS standardized mean difference (SMD) between SIJF and CM at three months and six months follow-up was - 1.4 [95% confidence interval - 2.3, - 0.6] and - 1.5 [95% CI - 1.8, - 1.1]. The ODI SMD between SIJF and CM scores at three months and 6 months follow-up was - 0.9 [95% CI - 1.1, - 0.7] and - 1.1 [95% CI - 1.6, - 0.5]. The odds ratio of adverse reactions due to SIJF compared to CM was 1.9 [95% CI 0.1, 42.8]. CONCLUSION: Based on the limited number of independent trials with long-term follow-up, SIJF shows potential as a surgical treatment option for SIJ pathology. PROSPERO REGISTRATION: CRD42020206149 (25th September 2020).

Optimizing the surgical instrument tray to immediately increase efficiency and lower costs in the operating room.

BACKGROUND: Surgical trays are often poorly configured and can be ongoing sources of frustration and excess costs. We conducted an observational study to determine if the use of a customized mathematical inventory optimization model would result in a greater reduction in the number of instruments on a surgical tray than a clinician review of the tray. METHODS: Utilization of instruments on the major orthopedic tray at a large academic hospital was documented over 80 procedures. Processes in the medical device reprocessing department and operating room were observed to comprehensively quantify all associated costs. Results of the observations were applied to a customized mathematical model to determine the ideal tray configuration. For comparison, a clinician review was also performed. RESULTS: The mathematical model alone produced an ideal tray size of 47 instruments, a reduction of 41 instruments from the original size of 88 instruments (47% reduction). This represented $34 440 in annual savings. In contrast, the clinician review alone suggested an ideal tray size of 67 instruments (23% reduction), representing $17 640 in annual savings. When clinicians were provided with the additional information from the model, they reduced the tray size to 51 instruments (42% reduction), producing $31 870 in savings. The mathematical model yielded an additional 22% instrument reduction and $14 230 in savings compared with clinician review alone. CONCLUSION: Our mathematical model is generalizable and can be applied to all specialties and hospitals to determine optimal tray configuration. As such, the financial implications are broad; at our institution, application to all surgical trays would result in $205 000 of savings annually. Surgeons and managers looking to streamline surgical trays should consider this evidence-based approach.

In-office needle arthroscopy is a cost-effective alternative for operating room diversion in medial meniscectomy: a financial analysis.

BACKGROUND: In-office needle arthroscopy (IONA) has been described as a diagnostic alternative to magnetic resonance imaging (MRI) for intra-articular pathology. However, few studies have analyzed its impact on cost and wait times when used as a therapeutic intervention. The purpose of this study was to investigate the impact on cost and wait times associated with offering IONA for partial medial meniscectomy as an alternative to traditional operating room (OR) arthroscopy for patients with irreparable medial meniscus tears on MRI. METHODS: Two models were created comparing the existing care pathway (current state) to a proposed future state utilizing IONA. Data sources were accounting data from an academically affiliated hospital in Canada and supplemented with literature values. A Monte Carlo simulation combined with DuPont analysis running 10,000 simulations was conducted to calculate the revenue, expenses, profits, and effect on surgical waitlists (i.e., throughput) between the states. Sensitivity analyses examined the influence of patient preference and revision rates on profit and throughput. Two-sample Student's t test was performed (p < .05). RESULTS: An average of 198 (standard deviation (SD) 31) patients underwent arthroscopic meniscectomy or repair each year from 2016 to 2020. The IONA revision rate was calculated as 20.3%. Compared to the current state, annual expenses in the IONA pathway were significantly reduced ($266,912.68 versus $281,415.23, p < .0001), while improving throughput by 21.2% (3.54%). Sensitivity analysis revealed 10% of patients need to select IONA over traditional OR arthroscopy with the revision rate remaining below 40% for the proposed state profit to be higher than the current state. CONCLUSIONS: IONA is a cost-effective alternative to traditional OR arthroscopy in patients undergoing partial medial meniscectomy. The next steps are to assess patients' perceptions of IONA as an alternative to traditional OR arthroscopy, and to carry out clinical trials to determine the efficacy, patient-reported outcome metrics, and complications of IONA.

Optimizing spine surgery instrument trays to immediately increase efficiency and reduce costs in the operating room.

Background: Over-crowded surgical trays result in perioperative inefficiency and unnecessary costs. While methodologies to reduce the size of surgical trays have been described in the literature, they each have their own drawbacks. In this study, we compared three methods: (1) clinician review (CR), (2) mathematical programming (MP), and (3) a novel hybrid model (HM) based on surveys and cost analysis. While CR and MP are well documented, CR can yield suboptimal reductions and MP can be laborious and technically challenging. We hypothesized our easy-to-implement HM would result in a reduction of surgical instruments in both the laminectomy tray (LT) and basic neurosurgery tray (BNT) that is comparable to CR and MP. Methods: Three approaches were tested: CR, MP, and HM. We interviewed 5 neurosurgeons and 3 orthopedic surgeons, at our institution, who performed a total of 5437 spine cases, requiring the use of the LT and BNT over a 4-year (2017-2021) period. In CR, surgeons suggested which surgical instruments should be removed. MP was performed via the mathematical analysis of 25 observations of the use of a LT and BNT tray. The HM was performed via a structured survey of the surgeons' estimated instrument usage, followed by a cost-based inflection point analysis. Results: The CR, MP, and HM approaches resulted in a total instrument reduction of 41%, 35%, and 38%, respectively, corresponding to total cost savings per annum of $50,211.20, $46,348.80, and $44,417.60, respectively. Conclusions: While hospitals continue to examine perioperative services for potential inefficiencies, surgical inventory will be increasingly scrutinized. Despite MP being the most accurate methodology to do so, our results suggest that savings were similar across all three methods. CR and HM are significantly less laborious and thus are practical alternatives.

A Dedicated Orthopaedic Trauma Room Improves Efficiency While Remaining Financially Net Positive.

OBJECTIVES: To determine the impact of dedicated orthopaedic trauma room (DOTR) implementation on operating room efficiency and finances. DESIGN: Retrospective cost-analysis. SETTING: Single midsized academic-affiliated community hospital in Toronto, Canada. PARTICIPANTS: All patients that underwent the most frequently performed orthopaedic trauma procedures (hip hemiarthroplasty, open reduction internal fixation of the ankle, femur, elbow and distal radius), over a 4-year period from 2016 to 2019 were included. INTERVENTION: Patient data acquired for 2 years before the implementation of a DOTR was compared with data acquired for a 2-year period after its implementation, adjusting for the number of cases performed. MAIN OUTCOME MEASUREMENTS: The primary outcome was surgical duration. The secondary outcome was financial impact, including after-hours costs incurred and opportunity cost of displaced elective surgeries. RESULTS: One thousand nine hundred sixty orthopaedic cases were examined pre- and post-DOTR. All procedures had reduced total operative time post-DOTR (mean improvement of 33.4%). The number of daytime operating hours increased 21%, whereas after-hours decreased by 37.8%. Overtime staffing costs were reduced by $24,976 alongside increase in opportunity costs of $22,500. This resulted in a net profit of $2476. CONCLUSIONS: Our results support the premise that DOTRs improve operating room efficiency and can be cost efficient. Our study also specifically addresses the hesitation regarding potential loss of profit from elective surgeries. Widespread implementation can improve patient care while still remaining financially favorable. LEVEL OF EVIDENCE: Economic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Machine learning models can predict subsequent publication of North American Spine Society (NASS) annual general meeting abstracts.

BACKGROUND CONTEXT: Academic meetings serve as an opportunity to present and discuss novel ideas. Previous studies have identified factors predictive of publication without generating predictive models. Machine learning (ML) presents a novel tool capable of generating these models. As such, the objective of this study was to use ML models to predict subsequent publication of abstracts presented at a major surgical conference. STUDY DESIGN/SETTING: Database study. METHODS: All abstracts from the North American Spine Society (NASS) annual general meetings (AGM) from 2013-2015 were reviewed. The following information was extracted: number of authors, institution, location, conference category, subject category, study type, data collection methodology, human subject research, and FDA approval. Abstracts were then searched on the PubMed, Google Scholar, and Scopus databases for publication. ML models were trained to predict whether the abstract would be published or not. Quality of models was determined by using the area under the receiver operator curve (AUC). The top ten most important factors were extracted from the most successful model during testing. RESULTS: A total of 1119 abstracts were presented, with 553 (49%) abstracts published. During training, the model with the highest AUC and accuracy metrics was the partial least squares (AUC of 0.77±0.05, accuracy of 75.5%±4.7%). During testing, the model with the highest AUC and accuracy was the random forest (AUC of 0.69, accuracy of 67%). The top ten features for the random forest model were (descending order): number of authors, year, conference category, subject category, human subjects research, continent, and data collection methodology. CONCLUSIONS: This was the first study attempting to use ML to predict the publication of complete articles after abstract presentation at a major academic conference. Future studies should incorporate deep learning frameworks, cognitive/results-based variables and aim to apply this methodology to larger conferences across other fields of medicine to improve the quality of works presented.

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review.

Background: There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care. Methods: A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted. Results: Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level. Conclusions: High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.

Standardization of laparoscopic trays using an inventory optimization model to produce immediate cost savings and efficiency gains.

Perioperative services comprise a large portion of hospital budgets; the procurement and processing of surgical inventories can be an area for optimization in operational inefficiency. Surgical instrument trays can be customized as procedure-specific or standardized as trays that can be used in numerous procedure types. We conducted an interventional study to determine the cost savings from standardizing laparoscopic surgery instrument trays. A single-period inventory optimization model was used to determine the configuration of a standardized laparoscopic (SL) tray and its minimal stock quantity (MSQ). Utilization of instruments on the general surgery, gynecology, and gynecological oncology trays was recorded, and daily demand for trays (mean, SD) was assessed using daily operating room (OR) case lists. Pre- and post-intervention costs were evaluated by reviewing procurement data and quantifying medical device reprocessing (MDR) and OR processes. The SL tray was trialled in the OR to test clinical safety and user satisfaction. Prior to standardization, the customized trays had a total inventory size of 391 instruments (mean instruments per tray: 17, range: 12-22). Daily demand was an MSQ of 23 trays. This corresponded to a procurement cost of $322,160 and reprocessing cost of $41,725. The SL tray (mean instruments per tray: 15, mean trays/day: 9.2 ± 3.2) had an MSQ of 17 trays/day. The total inventory decreased to 255 instruments, corresponding to a procurement cost of $266,900 with savings of $55,260 and reprocessing cost of $41,562 with savings of $163/year. After 33 trial surgeries, user satisfaction improved from 50% to 97% (p < .05). Standardization to a single SL tray using the inventory optimization model led to increased efficiency, satisfaction, and significant savings through aggregating specific service demands. The inventory optimization model could provide custom solutions for various institutions with the potential for large-scale financial savings. Thus, future work using this model at different centres will be necessary to validate these results.

An Anesthesia Block Room Is Financially Net Positive for a Hospital Performing Arthroplasty.

INTRODUCTION: Regional anesthesia is increasingly used in total joint arthroplasty (TJA). It has shown efficiency benefits as it allows parallel processing of patients in a dedicated block room (BR). However, granular quantification of these benefits to hospital operations is lacking. The goal of this study was to determine the financial effect of establishing a BR using comprehensive operational modeling. METHODS: A discrete-event simulation model of daily operating room (OR) patient flow for TJA procedures at a mid-sized hospital was developed. Two scenarios were tested: (1) without and (2) with a BR. Scenarios were compared according to staffing requirements, hours/day, and labor costs. The number of ORs and cases varied from 2 to 6 ORs performing 3 to 5 cases. These results were used as the inputs of a discounted cash flow (CF) model. Discounted CF model outputs were CF, net present value, internal rate of return, and return on investment. RESULTS: Mean time savings of incorporating a BR were 68 min/d (range: 30 to 80 min/d), reducing the OR closing time by 1 hour. Incremental labor costs/day from nurse overtime pay ranged from $2,025 to $10,125 with no BR and $1,595 to $9,045 with a BR, which resulted in an increase in profit/day from $360 to $1,605. The CF/annum was $54,363, the net present value was $213,082, the internal rate of return was 12%, and the return on investment was 43.61%. DISCUSSION: This study demonstrates that under all scenarios, a BR is more profitable than no BR to a hospital performing TJA via a bundled care or private payer remuneration model. A BR was shown to be financially net positive even when considering the necessary financial investment to establish it. In addition, this study demonstrates the potential of combining discrete-event simulation with financial analyses to assess various operational models of care to improve hospital efficiency, such as dedicated trauma rooms and swing rooms. LEVEL OF EVIDENCE: Level III.

Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty.

BACKGROUND: Total knee arthroplasty (TKA) is one of the most resource-intensive, high-volume surgical procedures. Two drivers of the cost of TKAs are duration of surgery (DOS) and postoperative inpatient length of stay (LOS). The ability to predict TKA DOS and LOS has substantial implications for hospital finances, scheduling, and resource allocation. The goal of this study was to predict DOS and LOS for elective unilateral TKAs using machine learning models (MLMs) based on preoperative factors. METHODS: The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for unilateral TKAs from 2014 to 2019. The dataset was split into training, validation, and testing based on year. Models (linear, tree-based, and multilayer perceptron (MLP)) were fitted to the training set in scikit-learn and PyTorch, with hyperparameters tuned on the validation set. The models were trained to minimize the mean squared error (MSE). Models with the best performance on the validation set were evaluated on the testing set according to 1) MSE, 2) buffer accuracy, and 3) classification accuracy, with results compared to a mean regressor. RESULTS: A total of 302,300 patients were included in this study. During validation, the PyTorch MLPs had the best MSEs for DOS (0.918) and LOS (0.715). During testing, the PyTorch MLPs similarly performed best based on MSEs for DOS (0.896) and LOS (0.690). While the scikit-learn MLP yielded the best 30-minute buffer accuracy for DOS (78.8%), the PyTorch MLP provided the best 1-day buffer accuracy for LOS (75.2%). Nearly all the ML models were more accurate than the mean regressors for both DOS and LOS. CONCLUSION: Conventional and deep learning models performed better than mean regressors for predicting DOS and LOS of unilateral elective TKA patients based on preoperative factors. Future work should include operational factors to improve overall predictions.

Inventory Optimization in the Perioperative Care Department Using Kotter's Change Model.

BACKGROUND: Perioperative services have been scrutinized in the context of cost containment in health care, particularly in the procurement and reprocessing of surgical instruments. Although solutions such as surgical instrument inventory optimization (IO) have been proposed, there is a paucity of literature on how to implement this change. The purpose of this project was to describe the implementation of an IO using Kotter's Change Model (KCM). METHODS: This study was conducted at a tertiary academic hospital across the four highest-volume surgical services. The IO was implemented using the steps outlined by KCM: (1) create coalition, (2) create vision for change, (3) establish urgency, (4) communicate the vision, (5) empower broad-based action, (6) generate short-term wins, (7) consolidate gains, and (8) anchor change. This process was evaluated using inventory metrics, operational efficiency metrics, and clinician satisfaction. RESULTS: Total inventory was reduced by 37.7%, with an average tray size reduction of 18.0%. This led to a total reprocessing time savings of 1,333 hours per annum and labor cost savings of $39,995 per annum. Depreciation cost savings were $64,320 per annum. Case cancellation rate due to instrument-related errors decreased from 3.9% to 0.2%. The proportion of staff completely satisfied with the inventory was 1.7% pre-IO and 80.0% post-IO. CONCLUSION: This is the first study to describe the successful implementation of KCM to facilitate change in the perioperative setting. This success contributes to the growing body of literature supporting KCM as a valuable change management tool in health care.

Megaprosthesis anti-bacterial coatings: A comprehensive translational review.

Periprosthetic joint infections (PJI) are catastrophic complications for patients with implanted megaprostheses and pose significant challenges in the management of orthopaedic oncology patients. Despite various preventative strategies, with the increasing rate of implanted orthopaedic prostheses, the number of PJIs may be increasing. PJIs are associated with a high rate of amputation. Therefore, novel strategies to combat bacterial colonization and biofilm formation are required. A promising strategy is the utilization of anti-bacterial coatings on megaprosthetic implants. In this translational review, a brief overview of the mechanism of bacterial colonization of implants and biofilm formation will be provided, followed by a discussion and classification of major anti-bacterial coatings currently in use and development. In addition, current in vitro outcomes, clinical significance, economic importance, evolutionary perspectives, and future directions of anti-bacterial coatings will also be discussed. Megaprosthetic anti-bacterial coating strategies will help reduce infection rates following the implantation of megaprostheses and would positively impact sarcoma care. STATEMENT OF SIGNIFICANCE: This review highlights the clinical challenges and a multitude of potential solutions to combating peri-prosthetic join infections in megaprotheses using anti-bacterial coatings. Reducing infection rates following the implantation of megaprostheses would have a major impact on sarcoma care and major trauma surgeries that require reconstruction of large skeletal defects.

Using Return on Investment Operational and Monte Carlo Modeling Techniques to Predict Financial Performance in a Tertiary Care Outpatient Clinic.

INTRODUCTION: The vast majority of health care quality improvement studies provide inadequate financial analysis to accurately predict a return on investment. We hypothesized that using return on invested capital operational mapping combined with a Monte Carlo simulation financial model could accurately predict institutional costs and operational metrics within an outpatient urology clinic. METHODS: A process map of a typical outpatient clinic visit was developed, and time studies were performed by following a sample of patients while considering all operational and financial variables that contributed to patient care. this process map was adapted into a return on invested capital-tree for financial modeling. Stochastic modeling using Monte Carlo simulation was performed to estimate financial metrics based on these operational and financial inputs for both the 2017-2018 and 2018-2019 fiscal years. These were then compared to the actual performance measures of those fiscal years. RESULTS: Combined return on invested capital-Monte Carlo simulation modeling generated financial and operational estimates that characterized the clinic's performance based on multivariable inputs. Most financial estimates for 2017-2018 differed by <4.31% from the actual financial values from that year. In predicting financial performance for 2018-2019, most of the estimated values were <7.67% different from their actual financial statement line items. CONCLUSIONS: As a proof of concept, this study demonstrated that a combined return on invested capital-operational mapping and Monte Carlo simulation modeling can predict key financial metrics in a tertiary care clinic. As such, common business tools can be useful in a health care setting when clinicians are evaluating how investments in quality improvement will influence their financial and operational performance.

Saturated Salt Solution Cadaver-Embalming Method Improves Orthopaedic Surgical Skills Training.

BACKGROUND: Human cadaver surgical skills training offers the highest-fidelity simulation of the operative environment, with the potential to enhance surgeon training and to reduce operative risks to patients. Embalming extends the duration that a cadaveric specimen may be used for surgical skills training and reduces the risk of disease transmission, but it can alter the properties of the cadaver tissue, reducing the simulation fidelity and training quality. The purpose of this controlled laboratory study was to evaluate 3 embalming methods, formaldehyde solution, alcohol-glycol solution, and saturated salt solution, and to compare their relative performance in a 2-week orthopaedic surgical skills training course. METHODS: The 3 embalming methods were applied to 3 cadavers each. Joint range of motion and joint stiffness were measured prior to dissection. The cadavers were assessed by 4 orthopaedic surgeons on the first and last days of the training course in 4 domains: tissue visual fidelity, tissue tactile fidelity, odor, and overall suitability for surgical skills training using 7-point Likert scales. Sterile specimens for bacterial and fungal culture were obtained from cadaver muscle tissue on the fourth and fifteenth days of use. RESULTS: All cadaver groups were successfully preserved without putrefaction and were utilized over the duration of the training course. The saturated salt solution cadavers had supple joints with excellent motion and were rated highly across all domains. The saturated salt solution cadaver joint motion, stiffness, visual and tactile tissue fidelity, odor, and suitability for surgical skills training were significantly superior (p < 0.05) to the formaldehyde solution and alcohol-glycol solution cadavers. CONCLUSIONS: The saturated salt embalming method is inexpensive and logistically straightforward and preserves human cadavers at room temperature in a state suitable for high-fidelity orthopaedic surgical skills training.

Pediatric Burns from Glass-Fronted Fireplaces in Canada: A Growing Issue Over the Past 20 Years.

There is an alarming lack of public awareness surrounding the safety of glass-fronted fireplaces. This has resulted in an active campaign from the American Burn Association Prevention Committee. One issue encountered while advocating for prevention among manufacturers is the lack of corroborating and accurate data. The purpose of this study was to examine the annual trends and epidemiology of glass-fronted fireplace-related burn injuries to children less than 15 years old, who presented to Canadian emergency departments between 1990 and 2010. Records of pediatric burn injuries related to glass-fronted fireplaces were extracted from the Canadian Hospitals Injury Reporting and Prevention Program database for the study period (1990-2010). Cases were analyzed in terms of anatomic area affected, demographics, seasonality, safety device use, and injury severity. A total of 616 cases of burns from glass-fronted fireplaces were identified. The incidence increased at an average of 2.7 cases per year. This is a greater than 20-fold increase over 20 years. Seventy-five percentage of the cases occurred in children less than 2 years, and 95% occurred in children less than 5 years. The study demonstrated a growing risk from glass-fronted fireplace burns, likely due to the increasing popularity of household gas fireplace units. These units are a particular risk to children less than 2 years, attributable to their developing mobility and reduced reaction time. This is a preventable injury that should be addressed through changes to legislation and manufacturing.