Real-World Performance Benchmarks in the Reading Ability of Prescription Drug Labels by Community-Dwelling Older Adults.

BACKGROUND: Despite known recommendations regarding standards for print size and the intuitive importance of vision in reading prescription labels, the predictive nature of vision and prescription label readability remains largely undefined. Furthermore, while the importance of vision is recognized, various demographic factors associated with the ability to read prescription labels have not been fully elucidated. OBJECTIVE: Describe relationships between visual acuity, point size, and readability of prescription labels and provide insight into demographic factors associated with prescription label readability. METHODS: Cross-sectional examination of prescription label readability by older, community-dwelling adults. Subjects were evaluated as to demographics, visual acuity, and ability to read test instruments consisting of unaltered prescription label features of five medications dispensed by community pharmacies and two drug samples. Descriptive statistics in conjunction with a logit predictive model were employed for data analysis. RESULTS: Instructions for medication use were most recognizable, identified and correctly read by 95.60% of the study cohort while directions for the use of drug samples were lowest (34.91%). Among prescription label features, auxiliary labels consistently demonstrated poor readability. Level of visual acuity was statistically related to the ability to read prescription labels while identifying prescription label components increased proportionally with point size. Race, gender, and history of a recent eye examination were statistically significant predictors of prescription label reading ability. Visual acuity alone was found to explain approximately 26% of the variablity in ability to read Rx labels. CONCLUSION: Visual acuity is predictive of the ability to access Rx label information and should be considered a modifiable variable for improving prescription label reading ability amenable by appropriate eye care and spectacle correction.

Effect of Age and Viscosity on Duration of Corneal Anesthesia with Topical 2% Lidocaine Administration.

Purpose: Formulation viscosity and patient-specific parameters such as age are important considerations in achieving patient comfort for prolonged anterior segment surgical procedures. In this study, we report pharmacodynamic and pharmacokinetic parameters of topical 2% lidocaine anesthetic decay based on formulation viscosity and subject age. Methods: Extemporaneous 2% lidocaine solution was compounded with varying percentages of carboxymethylcellulose (CMC) to adjust product viscosity. Juvenile and adult New Zealand White rabbits were utilized as a model for lidocaine-induced corneal anesthesia analysis. Following application of 20 µL in 1 eye of each animal, corneal sensitivity was measured using a Cochet-Bonnet esthesiometer at baseline and at 1-min intervals until recovery to baseline. Subsequent to washout period, the experiment was repeated for 3 replicate experiments. Results: A one-phase exponential decay model was utilized to describe rate of anesthesia decay. Bioavailability increased in a manner disproportionate to both tear film concentration and solution viscosity. In adult animals, half-life of anesthetic decay was found to range from 6.03 min with 2% lidocaine in 0.5% CMC to 9.45 min with 2% lidocaine in 1.5% CMC. In juveniles, half-life was found to be 4.46 and 3.58 min for 2% lidocaine in 1.5% CMC and commercial 2% lidocaine gel, respectively. Conclusions: Decay parameters of lidocaine-induced corneal anesthesia appear disparate from viscosity. It is postulated that viscosity-related increase in corneal contact time through reduced drainage plays a critical role in increasing bioavailability of topical anesthetics in our experimental findings, although nonlinear in character. Age is found to be an important mediator of lidocaine-induced corneal anesthesia.

Technical Skill Impacts the Success of Sequential Robotic Suturing Substeps.

Introduction: Robotic surgical performance, in particular suturing, has been associated with postoperative clinical outcomes. Suturing can be deconstructed into substep components (needle positioning, needle entry angle, needle driving, and needle withdrawal) allowing for the provision of more specific feedback while teaching suturing and more precision when evaluating suturing technical skill and prediction of clinical outcomes. This study evaluates if the technical skill required for particular substeps of the suturing process is associated with the execution of subsequent substeps in terms of technical skill, accuracy, and efficiency. Materials and Methods: Training and expert surgeons completed standardized sutures on the Mimic™ Flex virtual reality robotic simulator. Video recordings were deidentified, time annotated, and provided technical skill scores for each of the four suturing substeps. Hierarchical Poisson regression with generalized estimating equation was used to examine the association of technical skill rating categories between substeps. Results: Twenty-two surgeons completed 428 suturing attempts with 1669 individual technical skill assessments made. Technical skill scores between substeps of the suturing process were found to be significantly associated. When needle positioning was ideal, needle entry angle was associated with a significantly greater chance of being ideal (risk ratio [RR] = 1.12, p = 0.05). In addition, ideal needle entry angle and needle driving technical skill scores were each significantly associated with ideal needle withdrawal technical skill scores (RR = 1.27, p = 0.03; RR = 1.3, p = 0.03, respectively). Our study determined that ideal technical skill was associated with increased accuracy and efficiency of select substeps. Conclusions: Our study found significant associations in the technical skill required for completing substeps of suturing, demonstrating inter-relationships within the suturing process. Together with the known association between technical skill and clinical outcomes, training surgeons should focus on mastering not just the overall suturing process, but also each substep involved. Future machine learning efforts can better evaluate suturing, knowing that these inter-relationships exist.

Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery.

BACKGROUND: Our previous work classified a taxonomy of suturing gestures during a vesicourethral anastomosis of robotic radical prostatectomy in association with tissue tears and patient outcomes. Herein, we train deep learning-based computer vision to automate the identification and classification of suturing gestures for needle driving attempts. METHODS: Using two independent raters, we manually annotated live suturing video clips to label timepoints and gestures. Identification (2,395 videos) and classification (511 videos) datasets were compiled to train computer vision models to produce 2- and 5-class label predictions, respectively. Networks were trained on inputs of raw red/blue/green pixels as well as optical flow for each frame. Each model was trained on 80/20 train/test splits. RESULTS: In this study, all models were able to reliably predict either the presence of a gesture (identification, area under the curve: 0.88) as well as the type of gesture (classification, area under the curve: 0.87) at significantly above chance levels. For both gesture identification and classification datasets, we observed no effect of recurrent classification model choice (long short-term memory unit versus convolutional long short-term memory unit) on performance. CONCLUSION: Our results demonstrate computer vision's ability to recognize features that not only can identify the action of suturing but also distinguish between different classifications of suturing gestures. This demonstrates the potential to utilize deep learning computer vision toward future automation of surgical skill assessment.