Must Clinics Replace 2D by 3D Environments for an Efficient Training of Laparoscopic Novices? A Critical Analysis of the Learning Curve for Basic Skills

Background and AimsPublished studies repeatedly demonstrate an advantage of three-dimensional (3D) laparoscopic surgery over two-dimensional (2D) systems but with quite heterogeneous results. This raises the question whether clinics must replace 2D technologies to ensure effective training of future surgeons.MethodsWe recruited 45 students with no experience in laparoscopic surgery and comparable characteristics in terms of vision and frequency of video game usage. The students were randomly allocated to 3D (n = 23) or 2D (n = 22) groups and performed 10 runs of a laparoscopic “peg transfer” task in the Luebeck Toolbox. A repeated-measures ANOVA for operation times and a generalized linear mixed model for error rates were calculated. The main effects of laparoscopic condition and run, as well as the interaction term between the two, were examined.ResultsNo statistically significant differences in operation times and error rates were observed between 2D and 3D groups (p = 0.10 and p = 0.72, respectively). The learning curve showed a significant reduction in operation time and error rates (both p's < 0.001). No significant interactions between group and run were detected (operation time: p = 0.342, error rates: p = 0.83). With respect to both endpoints studied, the learning curves reached their plateau at the 7th run.ConclusionThe result of our study with laparoscopic novices revealed no significant difference between 2D and 3D technology with respect to performance time and the error rate in a simple standardized test. In the future, surgeons may thus still be trained in both techniques.

Improving Machine Learning Prediction of ADHD Using Gene Set Polygenic Risk Scores and Risk Scores from Genetically Correlated Phenotypes

Background: Polygenic risk scores (PRSs), which sum the effects of SNPs throughout the genome to measure risk afforded by common genetic variants, have improved our ability to estimate disorder risk for Attention-Deficit/Hyperactivity Disorder (ADHD) but the accuracy of risk prediction is rarely investigated. Methods: With the goal of improving risk prediction, we performed gene set analysis of GWAS data to select gene sets associated with ADHD within a training subset. For each selected gene set, we generated gene set polygenic risk scores (gsPRSs), which sum the effects of SNPs for each selected gene set. We created gsPRS for ADHD and for phenotypes having a high genetic correlation with ADHD. These gsPRS were added to the standard PRS as input to machine learning models predicting ADHD. We used feature importance scores to select gsPRS for a final model and to generate a ranking of the most consistently predictive gsPRS. Results: For a test subset that had not been used for training or validation, a random forest (RF) model using PRSs from ADHD and genetically correlated phenotypes and an optimized group of 20 gsPRS had an area under the receiving operating characteristic curve (AUC) of 0.72 (95% CI: 0.70 to 0.74). This AUC was a statistically significant improvement over logistic regression models and RF models using only PRS from ADHD and genetically correlated phenotypes. Conclusions: Summing risk at the gene set level and incorporating genetic risk from disorders with high genetic correlations with ADHD improved the accuracy of predicting ADHD. Learning curves suggest that additional improvements would be expected with larger study sizes. Our study suggests that better accounting of genetic risk and the genetic context of allelic differences results in more predictive models.

A Cost Model for 3D Woven Preforms

Lack of cost information is a barrier to acceptance of 3D woven preforms as reinforcements for composite materials, compared with 2D preforms. A parametric, resource-based technical cost model (TCM) was developed for 3D woven preforms based on a novel relationship equating manufacturing time and 3D preform complexity. Manufacturing time, and therefore cost, was found to scale with complexity for seventeen bespoke manufactured 3D preforms. Two sub-models were derived for a Weavebird loom and a Jacquard loom. For each loom, there was a strong correlation between preform complexity and manufacturing time. For a large, highly complex preform, the Jacquard loom is more efficient, so preform cost will be much lower than for the Weavebird. Provided production is continuous, learning, either by human agency or an autonomous loom control algorithm, can reduce preform cost for one or both looms to a commercially acceptable level. The TCM cost model framework could incorporate appropriate learning curves with digital twin/multi-variate analysis so that cost per preform of bespoke 3D woven fabrics for customised products with low production rates may be predicted with greater accuracy. A more accurate model could highlight resources such as tooling, labour and material for targeted cost reduction.

Learning from experience in positioning new computer products

The correct positioning of new computer products has become crucially important as markets saturate and competition intensifies. The logistic function can provide an aid to product positioning. The method presented her addresses questions of price and performance only, and involves determination of learning curves from data on past successful product launches. It assumes that companies learn like individuals and that variables such as performance/price grow according to logistic curves limited by the basic technologies at hand.Digital's experience shows that its VAX family of computers is amenable to such an analysis, which also provides insights on the overall evolution of that technology. Besides offering guidelines for product positioning, this approach provides a means for estimating price drops and/or performance enhancements necessitated from delays in product delivery.

Voice Versus Keyboard and Mouse for Text Creation on Arabic User Interfaces

Voice User Interfaces (VUIs) are increasingly popular owing to improvements in automatic speech recognition. However, the understanding of user interaction with VUIs, particularly Arabic VUIs, remains limited. Hence, this research compared user performance, learnability, and satisfaction when using voice and keyboard-and-mouse input modalities for text creation on Arabic user interfaces. A Voice-enabled Email Interface (VEI) and a Traditional Email Interface (TEI) were developed. Forty participants attempted pre-prepared and self-generated message creation tasks using voice on the VEI, and the keyboard-and-mouse modal on the TEI. The results showed that participants were faster (by 1.76 to 2.67 minutes) in pre-prepared message creation using voice than using the keyboard and mouse. Participants were also faster (by 1.72 to 2.49 minutes) in self-generated message creation using voice than using the keyboard and mouse. Although the learning curves were more efficient with the VEI, more participants were satisfied with the TEI. With the VEI, participants reported problems, such as misrecognitions and misspellings, but were satisfied about the visibility of possible executable commands and about the overall accuracy of voice recognition.

Learning curves in robot-assisted spine surgery: a systematic review and proposal of application to residency curricula

OBJECTIVE Spine robots have seen increased utilization over the past half decade with the introduction of multiple new systems. Market research expects this expansion to continue over the next half decade at an annual rate of 20%. However, because of the novelty of these devices, there is limited literature on their learning curves and how they should be integrated into residency curricula. With the present review, the authors aimed to address these two points. METHODS A systematic review of the published English-language literature on PubMed, Ovid, Scopus, and Web of Science was conducted to identify studies describing the learning curve in spine robotics. Included articles described clinical results in patients using one of the following endpoints: operative time, screw placement time, fluoroscopy usage, and instrumentation accuracy. Systems examined included the Mazor series, the ExcelsiusGPS, and the TiRobot. Learning curves were reported in a qualitative synthesis, given as the mean improvement in the endpoint per case performed or screw placed where possible. All studies were level IV case series with a high risk of reporting bias. RESULTS Of 1579 unique articles, 97 underwent full-text review and 21 met the inclusion and exclusion criteria; 62 articles were excluded for not presenting primary data for one of the above-described endpoints. Of the 21 articles, 18 noted the presence of a learning curve in spine robots, which ranged from 3 to 30 cases or 15 to 62 screws. Only 12 articles performed regressions of one of the endpoints (most commonly operative time) as a function of screws placed or cases performed. Among these, increasing experience was associated with a 0.24- to 4.6-minute decrease in operative time per case performed. All but one series described the experience of attending surgeons, not residents. CONCLUSIONS Most studies of learning curves with spine robots have found them to be present, with the most common threshold being 20 to 30 cases performed. Unfortunately, all available evidence is level IV data, limited to case series. Given the ability of residency to allow trainees to safely perform these cases under the supervision of experienced senior surgeons, it is argued that a curriculum should be developed for senior-level residents specializing in spine comprising a minimum of 30 performed cases.

Target-setting for technological development of the Russian aircraft industry in the field of countering climate change

Subject. The article discusses the introduction of a Pigouvian tax on greenhouse gas emissions. Objectives. The objective of the study is to develop methods for setting the target level of CO2 emissions by Russian aircraft, based on Russia's national interests (both economic and environmental). Methods. The emission target was set on the basis of the classical approach of determining the economically optimal level of pollution at the intersection of the curves of marginal damage from pollution and marginal costs of eliminating pollution. The assessment of marginal costs of reducing CO2 emissions was based on the learning curves in the field of research and development aimed at reducing emissions. Results. We developed a method to set up the target level of CO2 emissions by Russian aircraft based on Russia's national interests (economic and environmental), rather than on external requirements dictated by competitor nations. Conclusions. According to the calculations on the basis of realistic estimates of fixed costs for reducing the carbon dioxide emissions, the utility is maximized with a reduction of CO2 emissions by 10% (for this method of assessing the damage to the State and with the realistic estimates of fixed cost of reducing carbon dioxide emissions).

Distal Radial Artery Access for Coronary and Peripheral Procedures: A Multicenter Experience

Introduction: Distal radial access (dRA) has recently gained global popularity as an alternative access route for vascular procedures. Among the benefits of dRA are the low risk of entry site bleeding complications, the low rate of radial artery occlusion, and improved patient and operator comfort. The aim of this large multicenter registry was to demonstrate the feasibility and safety of dRA in a wide variety of routine procedures in the catheterization laboratory, ranging from coronary angiography and percutaneous coronary intervention to peripheral procedures. Methods: The study comprised 1240 patients who underwent coronary angiography, PCI or noncoronary procedures through dRA in two Hungarian centers from January 2019 to April 2021. Baseline patient characteristics, number and duration of arterial punctures, procedural success rate, crossover rate, postoperative compression time, complications, hospitalization duration, and different learning curves were analyzed. Results: The average patient age was 66.4 years, with 66.8% of patients being male. The majority of patients (74.04%) underwent a coronary procedure, whereas 25.96% were involved in noncoronary interventions. dRA was successfully punctured in 97% of all patients, in all cases with ultrasound guidance. Access site crossover was performed in 2.58% of the patients, mainly via the contralateral dRA. After experiencing 150 cases, the dRA success rate plateaued at >96%. Our dedicated dRA step-by step protocol resulted in high open radial artery (RA) rates: distal and proximal RA pulses were palpable in 99.68% of all patients at hospital discharge. The rate of minor vascular complications was low (1.5%). A threshold of 50 cases was sufficient for already skilled radial operators to establish a reliable procedural method of dRA access. Conclusion: The implementation of distal radial artery access in the everyday routine of a catheterization laboratory for coronary and noncoronary interventions is feasible and safe with an acceptable learning curve.