Subjective Metrics Research Articles

Flat-field methods for color image sensors

The research provides a detailed analysis of the primary sources of non-uniformity in color CMOS image sensors. It introduces the fundamental theory of traditional photon conversion and the main objective and subjective quality evaluation metrics for images. The theoretical foundations of traditional correction methods are also discussed. Based on the photon response characteristics of the color image sensor, a method for flat-field correction of color image sensors is proposed by analyzing the grayscale response curves of each pixel. This method addresses the non-linearity issue in pixel photon response caused by crosstalk between pixels and improves the non-uniformity problem in pixel response.

Subjective well-being indices in assessing happiness in Russia

Due to the multidimensionality and dynamism of today’s social life, there emerges the need to modify the principles of measuring the effectiveness of socio-economic policy. The article empirically tests and analyzes subjective metrics focused on “failures” in the socio-economic development which are impossible to identify through objective indicators only. These metrics are the subjective well-being indices for the Russian population in terms of gender and age, residence (by federal districts), level of education, marital status, presence of children, subjective health assessments, job and pay satisfaction. The methodological framework resides in Edward Diener’s subjective well-being theory. The method of index numbers and methods of descriptive statistics, including the multivariate frequency distribution of two attributes, are applied. The evidence base is comprised of panel data of The Russia Longitudinal Monitoring Survey – Higher School of Economics (RLMS-HSE) for 2013–2022. According to the research results, the minimum and maximum values of the life satisfaction index were observed in 2016 and 2022, respectively. The affective index of happiness was found to reach higher values compared to the cognitive assessment of it. The findings show that gender, age, region of residence, level of education, as well as satisfaction with health, job, wage and financial status affect the values and dynamics of happiness indices in Russia. A well-being measurement approach covering both objective and sub jective assessments of people’s life and its individual aspects allows identifying gaps in the system of socio-economic support for the population.

Visual cognition-based optimised design of primary flight displays in cockpits

Abstract The objective of this study was to investigate the effects of different cockpit primary flight display (PFD) interface designs on pilot cognitive efficiency and cognitive load. This study designed five optimised PFD interfaces and conducted interface cognition experiments to assess cognitive responses across six different PFD interface designs, including the original design. It compared various subjective and objective metrics across different interface designs and evaluated the impact of each design factor on cognitive task performance. The experimental results show that the PFD interface in the original interface design performs better under different flight symbol designs, and the interface with 50% increase in font size performs better among interface designs with different font sizes with relatively lower cognitive load. This study provides experimental support and optimization suggestions for the optimal design of cockpit PFD interface, which can help improve pilots’ perception and operational capabilities, and thus enhance task performance efficiency and flight safety. Future research can investigate the effects of various design factors on the cognitive effects of the interface to enhance the ongoing improvement and optimisation of interface design.

Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability.

The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article provides a systematic review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that HRV and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.

Pediatric contrast-enhanced chest CT on a photon-counting detector CT: radiation dose and image quality compared to energy-integrated detector CT.

Photon counting detector (PCD) CT benefits from reduced noise compared with conventional energy-integrating detector (EID) CT, which should translate to improved image quality and reduced radiation exposure for pediatric patients undergoing chest CT with IV contrast. To determine the differences in radiation exposure and image quality of PCD CT and EID CT in pediatric chest CT with intravenous (IV) contrast. In this institutional review board-approved retrospective observational study, 20 scan pairs (20 PCD CT; 20 EID CT) for children who underwent chest CT with IV contrast on both a PCD CT (Siemens NAEOTOM Alpha) and an EID CT (Siemens SOMATOM Definition Edge or Force) within 12months were reviewed independently by three pediatric radiologists for three subjective quality features on 5-point Likert scales: overall quality, small structure delineation, and motion artifact. Objective measures of image quality (image noise, signal-to-noise ratio, and contrast-to-noise ratio) were assessed by a single radiologist in several locations in the chest through region of interest measurement of Hounsfield units (HU) and standard deviation. Patient-related and radiation exposure parameters were collected for each scan and summarized with median and interquartile range (IQR). The Wilcoxon rank-sum test was utilized to compare groups. A P < 0.05 indicated statistical significance. Inter-observer agreement of subjective image quality metrics was analyzed using weighted kappa. Age (14.2years vs 13.8years, P= 0.15), height (P= 0.13), weight (P= 0.21), and BMI (P = 0.24) did not significantly differ between groups. There were 10 male and 3 female patients. Compared to EID CT, PCD CT showed lower radiation exposure parameters including volumetric CT dose index, 1.7mGy (IQR 1.1-2.4mGy) vs 3.8mGy (IQR 2.0-4.7mGy) (P< 0.01), and size-specific dose estimate, 2.6mGy (IQR 1.8-3.1mGy) vs 5.0mGy (IQR 3.3-6.2mGy) (P< 0.01). Objective image quality of lung parenchyma was improved on the PCD CT scanner, including image noise 119.5 HU (IQR 95.4-135.7 HU) vs 143.1 HU (IQR 125.4-169.8 HU) (P < 0.01), signal-to-noise ratio (SNR) -6.1 (IQR -8.4 to -4.8) vs -4.9 (IQR -5.6 to -3.8) (P= 0.01), and contrast-to-noise ratio -63.9 (-84.1 to -57.5) vs -60.5 (-76.3 to -52.5) (P = 0.01). Motion artifact was improved on the PCD CT scanner (P< 0.01). No significant differences in overall image quality or small structure delineation were identified (P= 0.06 and P= 0.31). PCD CT pediatric chest CT had significantly reduced radiation exposure, improved image quality, and reduced motion artifact compared with EID CT.

PAFPT: Progressive aggregator with feature prompted transformer for underwater image enhancement

The optical characteristics of underwater environments often result in distorted underwater images, which makes it difficult to meet the needs of complex underwater environment perception. The demand for obtaining high-quality underwater images presents challenges for existing underwater image enhancement methods. In this paper, a progressive aggregator with feature prompted transformer for underwater image enhancement is presented. First, a feature prompted transformer module consisting of a feature convolutional transposed attention and a prompted feedforward neural network is proposed. It integrates feature attention mechanisms and adjusts input features through prompt weights to better focus on global information while enhancing local details, enriching texture, removing blurring, and correcting color deviation. Concurrently, a progressive aggregator consisting of four stages of up-sampling aggregation blocks is constructed to transfer features effectively and facilitate the aggregation of features of different scales at different stages to realize comprehensive attention to image details and semantic information. Besides, a region reconstruction unit is designed, and a reconstruction attention mechanism is introduced to pay attention to the bottleneck localized region to suppress noise. The performance of our method is thoroughly evaluated on public underwater image datasets: EUVP, UIEB, RUIE and SQUID. The quantitative and qualitative results indicate that our method outperforms nine state-of-the-art methods in both subjective perception and evaluation metrics, demonstrating excellent learning and generalization abilities. Additionally, the excellent results reflect the significant performance gains it brings to downstream visual applications.

A fusion framework with multi-scale convolution and triple-branch cascaded transformer for underwater image enhancement

Acquiring high-quality underwater images is critical for various marine applications. However, light absorption and scattering problems in underwater environments severely degrade image quality. To address these issues, this study proposes a Fusion Framework with Multi-Scale Convolution and Triple-Branch Cascaded Transformer for Underwater Image Enhancement(FMTformer). This innovative framework incorporates multi-scale convolution and three-branch cascade transformer to enhance underwater images effectively. The FMTformer framework adds in the Multi-Conv Multi-Scale Fusion (MCMF) mechanism, which utilizes a spectrum of convolutional kernels to adeptly extract multi-scale features from both the base and detail layers of the decomposed image. This method ensures the capture of both high- and low-frequency information. Furthermore, this research introduces the Tri-Branch Self-Attention Transformer (TBSAT), designed to get cross-dimensional interactions via its Tri-Branch structure, significantly refines image processing quality. The framework also embedded the Value Reconstruct Cascade Transformer (VRCT), which refines feature map representation through mixed convolution, yielding enriched attention maps. Empirical evidence indicates that FMTformer achieves parity with the state-of-the-art in both subjective and objective evaluation metrics, outperforming extant methodologies.

Combining virtual reality with asymmetric collaborative learning: a case study in chemistry education

The use of Virtual Reality (VR) in education is getting more and more common, especially when hands-on learning experiences have to be delivered. With VR it becomes possible, e.g., to simulate dangerous or costly procedures that could hardly be implemented in real settings. However, engaging large classes in immersive laboratory activities may be difficult, since VR kits are still rather expensive for quantity purchases and may require powerful PCs as well as proper spaces to work. A possible way to deal with these issues could be to combine VR with so-called asymmetric Collaborative Learning (CL). CL is a particularly interesting pedagogical approach, as it make learners work in team to achieve a common educational goal, promoting critical thinking and active learning. In asymmetric CL, in particular, learners use different technologies to interact. When combined with VR, asymmetric CL could be used, e.g., to let some learners get immersed in a virtual environment, while some others actively participate in the experience using a desktop interface. This configuration could allow, in principle, to involve more learners in the same amount of time and with the same number of VR kits, while also letting them benefit of the advantages of CL. Based on these considerations, this paper investigates the impact of CL on VR-based education by leveraging an immersive virtual environment designed to support a laboratory experience in a Chemistry course. A user study was conducted by involving 46 university students enrolled in the course. Objective and subjective metrics were used to compare two education methods, i.e. one in which the students experienced the VR environment in isolation, another one in which pairs of students collaborated with an asymmetric approach. Students’ knowledge acquisition was assessed by means of theoretical quizzes, whereas practical performance was automatically measured during the VR experience. The experimental results showed that trading off VR-based, individual learning for CL may have positive effects on the acquisition of theoretical knowledge, but may be detrimental to the achievement of practical abilities if sufficient exposure to technology cannot be guaranteed.

Neuroergonomic Attention Assessment in Safety-Critical Tasks: EEG Indices and Subjective Metrics Validation in a Novel Task-Embedded Reaction Time Paradigm.

Background/Objectives: This study addresses the gap in methodological guidelines for neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices, including the engagement index (EI) and beta/alpha ratio, alongside subjective ratings. Methods: A novel task-embedded reaction time paradigm was developed to evaluate the sensitivity of these metrics to dynamic attentional demands in a more naturalistic multitasking context. By manipulating attention levels through varying secondary tasks in the NASA MATB-II task while maintaining a consistent primary reaction-time task, this study successfully demonstrated the effectiveness of the paradigm. Results: Results indicate that both the beta/alpha ratio and EI are sensitive to changes in attentional demands, with beta/alpha being more responsive to dynamic variations in attention, and EI reflecting more the overall effort required to sustain performance, especially in conditions where maintaining attention is challenging. Conclusions: The potential for predicting the attention lapses through integration of performance metrics, EEG measures, and subjective assessments was demonstrated, providing a more nuanced understanding of dynamic fluctuations of attention in multitasking scenarios, mimicking those in real-world safety-critical tasks. These findings provide a foundation for advancing methods to monitor attention fluctuations accurately and mitigate risks in critical scenarios, such as train-driving or automated vehicle operation, where maintaining a high attention level is crucial.

7038 Continuous Subcutaneous Hydrocortisone Infusion Is Practical And Effective In Select Patients With Adrenal Insufficiency

Abstract Disclosure: J.E. Lee: None. O. Hamidi: None. S. Mirfakhraee: None. Continuous subcutaneous hydrocortisone infusion (CSHI) via insulin pump is an alternative therapy for treating patients with adrenal insufficiency (AI) and allows for adjustable hydrocortisone delivery to provide more physiologic glucocorticoid replacement. Retrospective case series of 9 patients on CSHI showed a reduction in total daily dose of glucocorticoid by 34% and a decrease in hospitalization admissions due to adrenal crisis by 50%. In a double-blind placebo-controlled study, CSHI in patients with primary AI did not demonstrate benefit in subjective health status metrics compared to oral hydrocortisone; however, patients had good baseline subjective health scores. Due to a scarcity of data, CSHI is likely underutilized in patients who may benefit from this individualized therapy. Our research objectives were to analyze the change in total daily glucocorticoid dose, number of adrenal crises events and hospitalization days, glucocorticoid-related comorbidities, and quality of life of patients transitioned to CSHI. We performed a single-center, retrospective longitudinal follow-up study in 23 consecutive patients (87% women, median age 40 years at time of CSHI initiation) treated with CSHI between 2015 and 2023. Types of adrenal insufficiency were 48% secondary AI, 35% glucocorticoid induced AI, and 17% primary AI. CSHI delivery settings were generated by a formula that we derived using each patient’s daily oral glucocorticoid dose and preferred waking time. Median time from AI diagnosis to CSHI implementation was 41 months, and median duration of CSHI was 25 months. Total daily dose of glucocorticoid (in hydrocortisone equivalent) before CSHI vs during CSHI decreased from 30.0mg (15.0mg to 80.0mg) to 26.6mg (14.4mg to 200.5mg) (p= 0.45). Median number of adrenal crisis events decreased from 1 (0 to 13) to 0 (0 to 38) (p=0.25), and hospitalization days due to adrenal crisis decreased from 2 (0 to 46) to 0 (0 to 58) (p=0.53). No significant differences were noted for change in weight, blood pressure, diabetes, cardiovascular and cerebrovascular events, total cholesterol, LDL, and triglyceride levels. Median HDL decreased from 59 to 51 (p=0.01). In terms of subjective health status, 10 patients completed SF-36 survey while on CSHI. Significant impairments were noted in physical health, vitality, and general health with median score less than 40. At the conclusion of the study, 20 patients (87%) preferred CSHI therapy. Only 3 patients stopped CSHI for reasons of inconvenience, skin irritation at pump site, and better symptom control on oral therapy. No significant CSHI-related safety concerns were noted. In conclusion, CSHI is a safe and effective way to deliver individualized therapy to patients with difficult to control AI. CSHI led to reduction in glucocorticoid exposure and fewer adrenal crisis events and hospitalization days due to adrenal crisis. Presentation: 6/1/2024

A Survey on the Design of Virtual Reality Interaction Interfaces

Virtual reality (VR) technology has made remarkable progress in recent years and will be widely used in the future. As a bridge for information exchanges between users and VR systems, the interaction interface is pivotal for providing users with a good experience and has emerged as a key research focus. In this review, we conducted a comprehensive search of the Web of Science and CNKI databases from 2011 to 2023 to identify articles dedicated to VR interaction interface design. Through a meticulous analysis of 438 articles, this paper offers a substantial contribution to the emerging field of VR interactive interface research, providing an in-depth review of the principal research advancements. This review revealed that the majority of studies are centered on practical case analyses within specific application scenarios, employing empirical evaluation methods to assess objective or subjective metrics. We then concentrated on elucidating the foundational principles of interface design and their evaluation methodologies, providing a reference for future research endeavors. Additionally, the limitations, challenges, and future directions in VR interaction interface design research were discussed, highlighting the need for further research in design evaluation to continuously refine the development of standards and guidelines for VR interactive interface design. According to the findings of this review, there is a necessity to enhance research on information design for multi-channel interactive interfaces. Furthermore, it is essential to focus on the diverse characteristics of users to propose more inclusive design solutions. Adopting interdisciplinary approaches could lead to breakthroughs in the creation of personalized and adaptive VR interaction interfaces.

Building a Graphical Modelling Language for Efficient Homomorphic Encryption Schema Configuration: HomoLang

Homomorphic encryption (HE) is an emerging technology that enables computing on data while the data is encrypted. It has advantages, but it also has a significant difficulty. Programmers that use General-Purpose Programming Languages (GPPLs) may find it difficult to handwrite the script code for the HE correctly. This paper presents the front-end compiler design for the first graphical modelling language (DSML) to implement HE schemas, called HomoLang. It is providing a graphical environment with graphical building nodes that represent the HE concepts to enable the building of HE schemas. A high degree of abstraction and a decrease in grammatical and runtime errors improved the expressiveness and efficiency of implementation. Six security tests for security analysis were provided. The efficiency of the submitted language was evaluated using four subjective metrics. This paper provides a detailed explanation of the attributes, evaluation details, and design of the submitted HomoLang.

Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning

Notification systems that convey urgency without adding cognitive burden are crucial in human–computer interaction. Haptic feedback systems, particularly those utilizing vibration feedback, have emerged as a compelling solution, capable of providing desirable levels of urgency depending on the application. High-risk applications require an evaluation of the urgency level elicited during critical notifications. Traditional evaluations of perceived urgency rely on subjective self-reporting and performance metrics, which, while useful, are not real-time and can be distracting from the task at hand. In contrast, EEG technology offers a direct, non-intrusive method of assessing the user’s cognitive state. Leveraging deep learning, this study introduces a novel approach to evaluate perceived urgency from single-trial EEG data, induced by vibration stimuli on the upper body, utilizing our newly collected urgency-via-vibration dataset. The proposed model combines a 2D convolutional neural network with a temporal convolutional network to capture spatial and temporal EEG features, outperforming several established EEG models. The proposed model achieves an average classification accuracy of 83% through leave-one-subject-out cross-validation across three urgency classes (not urgent, urgent, and very urgent) from a single trial of EEG data. Furthermore, explainability analysis showed that the prefrontal brain region, followed by the central brain region, are the most influential in predicting the urgency level. A follow-up neural statistical analysis revealed an increase in event-related synchronization (ERS) in the theta frequency band (4–7 Hz) with the increased level of urgency, which is associated with high arousal and attention in the neuroscience literature. A limitation of this study is that the proposed model’s performance was tested only the urgency-via-vibration dataset, which may affect the generalizability of the findings.

Kurdish end-to-end speech synthesis using deep neural networks

This article introduces an end-to-end text-to-speech (TTS) system for the low-resourced language of Central Kurdish (CK, also known as Sorani) and tackles the challenges associated with limited data availability. We have compiled a dataset suitable for end-to-end text-to-speech that includes 21 h of CK female voice paired with corresponding texts. To identify the optimal performing system, we employed Tacotron2, an end-to-end deep neural network for speech synthesis, in three training experiments. The process involves training Tacotron2 using a pre-trained English system, followed by training two models from scratch with full and intonationally balanced datasets. We evaluated the effectiveness of these models using Mean Opinion Score (MOS), a subjective evaluation metric. Our findings demonstrate that the model trained from scratch on the full CK dataset surpasses both the model trained with the intonationally balanced dataset and the model trained using a pre-trained English model in terms of naturalness and intelligibility by achieving a MOS of 4.78 out of 5.

Revisiting the Heider and Simmel experiment for social meaning attribution in virtual reality

In their seminal experiment in 1944, Heider and Simmel revealed that humans have a pronounced tendency to impose narrative meaning even in the presence of simple animations of geometric shapes. Despite the shapes having no discernible features or emotions, participants attributed strong social context, meaningful interactions, and even emotions to them. This experiment, run on traditional 2D displays has since had a significant impact on fields ranging from psychology to narrative storytelling. Virtual Reality (VR), on the other hand, offers a significantly new viewing paradigm, a fundamentally different type of experience with the potential to enhance presence, engagement and immersion. In this work, we explore and analyze to what extent the findings of the original experiment by Heider and Simmel carry over into a VR setting. We replicate such experiment in both traditional 2D displays and with a head mounted display (HMD) in VR, and use both subjective (questionnaire-based) and objective (eye-tracking) metrics to record the observers’ visual behavior. We perform a thorough analysis of this data, and propose novel metrics for assessing the observers’ visual behavior. Our questionnaire-based results suggest that participants who viewed the animation through a VR headset developed stronger emotional connections with the geometric shapes than those who viewed it on a traditional 2D screen. Additionally, the analysis of our eye-tracking data indicates that participants who watched the animation in VR exhibited fewer shifts in gaze, suggesting greater engagement with the action. However, we did not find evidence of differences in how subjects perceived the roles of the shapes, with both groups interpreting the animation’s plot at the same level of accuracy. Our findings may have important implications for future psychological research using VR, especially regarding our understanding of social cognition and emotions.

A novel digital tool for detection and monitoring of amyotrophic lateral sclerosis motor impairment and progression via keystroke dynamics

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative condition leading to progressive muscle weakness, atrophy, and ultimately death. Traditional ALS clinical evaluations often depend on subjective metrics, making accurate disease detection and monitoring disease trajectory challenging. To address these limitations, we developed the nQiALS toolkit, a machine learning-powered system that leverages smartphone typing dynamics to detect and track motor impairment in people with ALS. The study included 63 ALS patients and 30 age- and sex-matched healthy controls. We introduce the three core components of this toolkit: the nQiALS-Detection, which differentiated ALS from healthy typing patterns with an AUC of 0.89; the nQiALS-Progression, which separated slow and fast progression at specific thresholds with AUCs ranging between 0.65 and 0.8; and the nQiALS-Fine Motor, which identified subtle progression in fine motor dysfunction, suggesting earlier prediction than the state-of-the-art assessment. Together, these tools represent an innovative approach to ALS assessment, offering a complementary, objective metric to traditional clinical methods and which may reshape our understanding and monitoring of ALS progression.

Characteristics Associated with Successful Residency Match in General Surgery.

To evaluate characteristics of matched and unmatched general surgery residency (GSR) applicants. Given the recent change of the United States Medical Licensing Exam Step 1 grading to pass/fail, understanding the factors that influence GSR match success is integral to identifying potential interventions to improve match rates for diverse medical students. Retrospective review of GSR National Residency Matching Program (NRMP) applicant and Accreditation Council for Graduate Medical Education (ACGME) active resident data between 2011 and 2022. Data included application characteristics for United States ("US") and "independent" applicants, factors cited by program directors in the interview and ranking process, paths pursued if applicants went unmatched, and racial/ethnic representation. A total of 9149 US and 3985 independent applicants applied to GSR between 2011 and 2021. Matched versus unmatched applicants had higher step 1 scores (US: 236 vs 218, P = 0.005; independent: 237 vs 228, P = 0.001), higher step 2 scores (US: 248 vs 232, P = 0.006; independent: 245 vs 234, P < 0.001), more likely to belong to alpha omega alpha (US: 17.1% vs 1.6%, P = 0.002) or to attend a top 40 National Institutes of Health-funded school (US: 31.0% vs 19.4%, P = 0.002) compared to unmatched applicants. Program directors heavily factored step 1 and step 2 scores, letters of recommendation, interactions with faculty and trainees, and interpersonal skills when interviewing and ranking applicants. The proportion of active general surgery residents versus applicants was lower for Asians (12.3% vs 20.9%, P < 0.001), Black/African American (5.0% vs 8.8%, P < 0.001), Hispanic/Latino (5.0% vs 9.4%, P = 0.001), and underrepresented in medicine students (10.3% vs 19.1%, P < 0.001). In the pass/fail step 1 era, factors including step 2 score and other subjective metrics may be more heavily weighted in the GSR match process.

Research on a Multidimensional Digital Printing Image Quality Evaluation Method Based on MLP Neural Network Regression

High-quality printing is a longstanding objective in the printing and replication industry. However, the methods used to evaluate print quality suffer from subjectivity and multidimensionality, relying on personal preferences and subjective perceptions to assess the quality of printed images, which poses significant limitations. To address these issues, a set of evaluation metrics aimed at assessing the quality of digital printing products is proposed to achieve evaluation results consistent with human visual perception. Given the differing imaging principles of pre-press digital images and post-scan images, these images are first preprocessed to standardize them for comparison. Next, features are extracted in both spatial and frequency domains, and similarity metrics are used to quantify the differences in features between pre-press digital images and post-scan images. Finally, a multilayer perceptron (MLP) neural network regression model is trained to predict the final objective quality scores. Experimental results on two standard databases demonstrate that this metric exhibits high consistency in both subjective and objective quality evaluation metrics for printed image quality assessment and outperforms other metrics in terms of accuracy.

The development of Logix – An application for component-based case logging and surgical trainee assessment

BackgroundThe optimal training program to transform a new resident into a competent and capable surgeon is constantly evolving. Competency-based evaluation represents a change in mindset from quantitative or chronologic metrics for graduate readiness. As surgery becomes more specialized, more dependent on technology, and more public, we must continue to improve our ability to pass on technical skills. Approaching surgery in a component-based fashion enables even the most complex operation to be broken down into smaller sets of steps that range the entire spectrum of complexity. Treating an operation through the lens of its components, emphasizing stepwise forward progression in a trainee's experience, may provide a way to train competent surgeons more efficiently. Current case-logging products do not provide adequate granularity to apply this methodology. MethodsApplication design relied on the involvement of local surgeons from all specialties and subspecialties related to general surgical training. Individual interviews with multiple experts in each field were used to generate a list of most commonly performed operations. Once a consensus was reached, the same surgeons were queried on what they felt were the core steps that make up each operation. This information was utilized to create a novel mobile application which enables the user to record cases by date, attending surgeon, specific operation, and which portions of the operation they were able/allowed to perform. ConclusionComponent-based case logging through the Logix application may be a useful adjunct as we continue to implement competency-based surgical training. Future investigation will assess user experience and compare subjective and objective metrics of training progression between the Logix application and currently utilized products. The information provided by the application stands to benefit not just trainees, but educators, training programs, and regulatory bodies. Key messageComponent-based case logging via a novel mobile application stands to increase the efficiency of surgical training and more effectively assess trainee competency.

Pharmacologic Sleep Aids in the Intensive Care Unit: A Systematic Review.

Background: Patients in the intensive care unit (ICU) often experience poor sleep quality. Pharmacologic sleep aids are frequently used as primary or adjunctive therapy to improve sleep, although their benefits in the ICU remain uncertain. This review aims to provide a comprehensive assessment of the objective and subjective effects of medications used for sleep in the ICU, as well as their adverse effects. Methods: PubMed, Web of Science, Scopus, Embase, and Cochrane Central Register of Controlled Trials were systematically searched from their inception until June 2023 for comparative studies assessing the effects of pharmacologic sleep aids on objective and subjective metrics of sleep. Results: Thirty-four studies with 3498 participants were included. Medications evaluated were melatonin, ramelteon, suvorexant, propofol, and dexmedetomidine. The majority of studies were randomized controlled trials. Melatonin and dexmedetomidine were the best studied agents. Objective sleep metrics included polysomnography (PSG), electroencephalography (EEG), bispectral index, and actigraphy. Subjective outcome measures included patient questionnaires and nursing observations. Evidence for melatonin as a sleep aid in the ICU was mixed but largely not supportive for improving sleep. Evidence for ramelteon, suvorexant, and propofol was too limited to offer definitive recommendations. Both objective and subjective data supported dexmedetomidine as an effective sleep aid in the ICU, with PSG/EEG in 303 ICU patients demonstrating increased sleep duration and efficiency, decreased arousal index, decreased percentage of stage N1 sleep, and increased absolute and percentage of stage N2 sleep. Mild bradycardia and hypotension were reported as side effects of dexmedetomidine, whereas the other medications were reported to be safe. Several ongoing studies have not yet been published, mostly on melatonin and dexmedetomidine. Conclusions: While definitive conclusions cannot be made for most medications, dexmedetomidine improved sleep quantity and quality in the ICU. These benefits need to be balanced with possible hemodynamic side effects.