Higher Performance Scores Research Articles

Abstract #1413640: Artificial Intelligence and Machine Learning in the Detection of Polycystic Ovarian Syndrome: A Systematic Review and Meta-Analysis

Polycystic Ovarian Syndrome (PCOS) affects 5-20% of reproductive aged women but remains widely underdiagnosed. Moreover, artificial intelligence (AI) and machine learning (ML) have emerged as great supplemental tools in healthcare settings. To identify the utility of AI/ML in the diagnosis or classification of PCOS, we conducted a systematic review of the literature. We searched the following databases by applying relevant keywords - Embase, Cochrane Register, Web of Science and the IEEE Xplore Digital Library. Relevant studies were extracted and synthesized from inception up to January 1, 2022. Studies that used a clinical comparator criterion (NIH, Rotterdam, or Revised International PCOS classification) were considered to ‘diagnose’ and those without such a comparator were considered to ‘classify’ PCOS. Performance characteristics for each study were extracted and analyzed. A total of 31 studies were included in the final analysis out of 135 studies that were initially screened and extracted. Sources of data for AI/ML interventions included clinical information, electronic medical records, biochemical and genetic data. A total of ten studies (32%) diagnosed PCOS used established diagnostic criteria while the remaining 21 (68%) classified women with PCOS. Additionally, 17 (55%) studies utilized clinical data with or without imaging. Support vector machine (42% of studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML techniques. Area under the ROC (plotted against clinical diagnosis) ranged between 73% and 100% (n=7 studies), diagnostic accuracy was between 89% and 100% (n=4 studies). Moreover, sensitivity ranged between from 41% and 100% (n=10 studies), specificity between 75% and 100% (n=10), positive predictive value (PPV) between 68% and 95% (n=4), and negative predictive value (NPV) between 94% and 99% (n=2 studies). Artificial intelligence and machine learning had high-performance scores in detecting PCOS and thus hold promise in reducing the burden of underdiagnosed PCOS. However, future AI and ML based studies should employ established PCOS criteria as comparators and adhere to methodological guidelines to improve their application and deployment in clinical settings.

Traffic event detection as a slot filling problem

Social media platforms, such as Twitter, can be used to extract information related to traffic events. Previous works focused mainly on classifying tweets into predefined categories (i.e., traffic or non-traffic) without many details of traffic events. However, extracting traffic-related fine-grained information from tweets is essential to build an intelligent transportation system. In this work, we address for the first time the problem of detecting traffic events using Twitter as two subtasks: (i) identifying whether a tweet is traffic-related or not as a text classification subtask, and (ii) extracting more fine-grained information (i.e., “what”, “when”, “where”, and the “consequence” of the traffic event) as a slot filling subtask. We also publish two Dutch Traffic Twitter datasets from Belgium and the Brussels capital region. We propose using deep learning based methods that process the two subtasks separately or jointly. Experimental results indicate that the proposed architectures achieve high performance scores (i.e., more than 95% F1 score) on the constructed datasets for both subtasks, even in a transfer learning scenario. In addition, incorporating tweet-level information in each of the tokens comprising the tweet (for the BERT-based model) can lead to a performance improvement for the joint setting. Our datasets and code are available on GitHub.11https://github.com/Glovesme/TrafficEventDetectionBE.

Evaluation of near-surface air temperature reanalysis datasets and downscaling with machine learning based Random Forest method for complex terrain of Turkey

Near-surface air temperature is a key variable used in a wide range of applications showing the environmental conditions of the Earth. Standard meteorological observations generally provide the best estimation with high accuracy over time for a small area of influence. However, considerable uncertainty arises when point measurements are extrapolated or interpolated over much larger areas. Satellite and reanalysis products have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. Thus, spatial patterns of air temperature can be derived from these products.In this study, we evaluate the performance of several reanalysis products of near-surface air temperature to determine the best product in estimating daily and monthly air temperatures across the complex terrain of Turkey. European Center for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), (AgERA5), (ERA5-Land), the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2), and the Japanese 55-year Reanalysis (JRA-55) products are assessed with 1120 ground-based gauge stations for the period 2015–2019 over complex terrain having different climate classes according to Köppen-Geiger classification scheme and land surface types. Moreover, several traditional and more sophisticated machine learning downscaling algorithms are applied to increase the products’ spatial resolution. The agreement between ground observations and the different products and the downscaled temperature product is investigated by using a set of commonly employed statistical estimators of mean absolute error (MAE), correlation coefficient (CC), root-mean-square error (RMSE), and bias.Performance analysis of reanalysis air temperature products with ground-based observations on daily and monthly time series has shown that among the five datasets, the AgERA5 product is superior in estimating air temperature over Turkey, both seasonally and annually. Compared to the other datasets, ERA5 and ERA5-Land show similar performance and reach the second highest performance score at both daily and monthly time steps. In terms of improving product performance with spatial downscaling, among the distance-based methods, the best overall performance is obtained by bicubic interpolation with a slight increase in the product performance in monthly and daily time series. However, depending on the season, the Random Forest algorithm's performance is far superior to all other methods used in this study.

Primary Health Care assessment in the COVID-19 pandemic from physicians' and nurses' perspective.

to assess the operationalization of Primary Health Care in the COVID-19 pandemic, according to Primary Care Assessment Tool: PCATool-Brasil attributes, from physicians' and nurses' perspective. a cross-sectional study, carried out with 99 physicians and nurses from Basic Health Units in a state in northeastern Brazil, with the aid of the adapted instrument PCATool-Brasil. Essential Score was classified as high performance (6.6) and General Score as low performance (6.5). First Contact Access, Care Integration, and Community Guidance scores were <6.6. The best performances were attributed to the Longitudinality, Comprehensiveness and Family Guidance services (scores>6.6). the attributes of Primary Health Care, in general, showed values above or close to the cut-off point in the assessment. These data can support strategies for local and national managers to strengthen Primary Health Care in the COVID-19 pandemic and future public health emergencies.

Total Elbow Arthroplasty in Young Patients - An 8-Year Follow-up Case Series.

Certain elbow pathologies can give rise to considerable loss of joint function, which would severely affect the daily living activities of the patients. Total elbow arthroplasty (TEA) can be a safe and reasonable motion-preserving and pain-relieving option in such debilitating elbow conditions. The aim of this case series is to evaluate the functional outcome of TEA in young patients with four different indications after an 8-year follow-up. We report a case series of four TEA cases done for different indications, namely rheumatoid arthritis, chronic elbow dislocation, post-traumatic elbow ankylosis, and failed primary total elbow replacement, all in relatively young patients. All patients were given a semi-constrained Coonrad-Morrey prosthesis using a posterior Bryan-Morrey approach. The cases were done during the years 2013-2014. Eight-year follow-up of each case showed excellent functional outcome and persistent pain relief with a high Mayo Elbow Performance Score. All the patients have given written consent for publishing this article. Despite being uncommon, total elbow replacement can be the choice of surgery for certain elbow affections, giving satisfactory functional recovery and good pain relief, even in a younger population.

Machine Learning-Enabled Framework for High-Throughput Screening of MOFs: Application in Radon/Indoor Air Separation.

Radon and its progeny may cause severe health hazards, especially for people working in underground spaces. Therefore, in this study, a hybrid artificial intelligence machine learning-enabled framework is proposed for high-throughput screening of metal-organic frameworks (MOFs) as adsorbents for radon separation from indoor air. MOFs from a specific database were initially screened using a pore-limiting diameter filter. Subsequently, random forest classification and grand canonical Monte Carlo simulations were implemented to identify MOFs with a high adsorbent performance score (APS) and high regenerability (R %). Interpretability and trustworthiness were determined by variable importance analysis , and adsorption mechanisms were elucidated by calculating the adsorption sites using Materials Studio. Notably, two MOF candidates were discovered with higher APS values in both the radon/N2 and radon/O2 systems compared with that of ZrSQU which is the best-performing MOF thus far, with R % values exceeding 85%. Furthermore, the proposed framework can be flexibly applied to multiple data sets due to good performance in model transfer. Therefore, the proposed framework has the potential to provide guidelines for the strategic design of MOFs for radon separation.

Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust.

Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.

Growth responses and genetic variation among highly ecologically diverse spring wheat genotypes grown under seawater stress.

Most of the freshwaters worldwide are used for agriculture. Freshwater sources are expected to decline and will not suffice to support the food production needed for the growing population. Therefore, growing crops with seawater might constitute a solution. However, very little work has been done on the effect of seawater stress on wheat, an important cereal crop. The present study aimed to determine whether particular wheat genotypes provided better resistance to seawater stress. A set of 80 highly diverse spring wheat genotypes collected from different countries in Europe, Asia, Africa, North and South America was exposed to 50% seawater stress at the early growth stage. Four seeding shoot and root traits were scored for all genotypes. High genetic variations were found among all genotypes for the epicotyl length (EL), hypocotyl length (HL), number of radicles (NOR), and fresh weight (FW). Eight genotypes with high-performance scores of seedling traits were selected. The correlation analyses revealed highly significant correlations among all traits scored in this study. The strongest correlation was found between the NOR and the other seeding traits. Thus, the NOR might be an important adaptive trait for seawater tolerance. The genetic diversity among all genotypes was investigated based on genetic distance. A wide range of genetic distances among all genotypes was found. There was also a great genetic distance among the eight selected genotypes. In particular, the genetic distance between ATRI 5310 (France) and the other seven genotypes was the greatest. Such high genetic diversity might be utilized to select highly divergent genotypes for crossing in a future breeding program. The present study provides very useful information on the presence of different genetic resources in wheat for seawater tolerance.

Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation

Abstract. This paper presents an innovative approach, STREAM – SaTellite-based Runoff Evaluation And Mapping – to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and total water storage anomalies (TWSAs). Within a very simple model structure, precipitation and soil moisture data are used to estimate the quick-flow river discharge component while TWSAs are used for obtaining its complementary part, i.e., the slow-flow river discharge component. The two are then added together to obtain river discharge estimates. The method is tested over the Mississippi River basin for the period 2003–2016 by using precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), soil moisture data from the European Space Agency's Climate Change Initiative (ESA CCI), and total water storage data from the Gravity Recovery and Climate Experiment (GRACE). Despite the model simplicity, relatively high-performance scores are obtained in river discharge estimates, with a Kling–Gupta efficiency (KGE) index greater than 0.64 both at the basin outlet and over several inner stations used for model calibration, highlighting the high information content of satellite observations on surface processes. Potentially useful for multiple operational and scientific applications, from flood warning systems to the understanding of water cycle, the added value of the STREAM approach is twofold: (1) a simple modeling framework, potentially suitable for global runoff monitoring, at daily timescale when forced with satellite observations only, and (2) increased knowledge of natural processes and human activities as well as their interactions on the land.

Implementation of the Hospital Incident Command System during COVID-19 Pandemic; Experience from an Iranian Reference Hospital

Introduction: There is growing popularity of the Hospital Incident Command System (HICS) as an organizational tool for hospital management in the COVID-19 pandemic. We specifically describe implementation of HICS at the Isfahan province reference hospital (Isabn-e-Maryam) during the COVID-19 pandemic and try to explore performance of it. Methods: To document the actions taken during the COVID-19 pandemic, standard, open-ended interviews were conducted with individuals occupying activated HICS leadership positions during the event. A checklist based on the job action sheets of the HICS was used for performance assessment. Results: With the onset of the pandemic, hospital director revised ICS structure that adheres to span of better control of COVID-19. Methods of expanding hospital inpatient capacity to enable surge capacity were considered. The highest performance score was in the field of planning. Performance was intermediate in Financial/Administration section and good in other fields. Discussion: In the current COVID-19 pandemic, establishing HICS with some consideration about long-standing events can help improve communication, resource use, staff and patient protection, and maintenance of roles.

Critical appraisal of minimally invasive keyhole surgery for intracranial meningioma in a large case series.

Meningioma surgery has evolved over the last 20 years with increased use of minimally invasive approaches including the endoscopic endonasal route and endoscope-assisted and gravity-assisted transcranial approaches. As the "keyhole" concept remains controversial, we present detailed outcomes in a cohort series. Retrospective analysis was done for all patients undergoing meningioma removal at a tertiary brain tumor referral center from 2008-2021. Keyhole approaches were defined as: use of a minimally invasive "retractorless" approach for a given meningioma in which a traditional larger approach is often used instead. The surgical goal was maximal safe removal including conservative (subtotal) removal for some invasive locations. Primary outcomes were resection rates, complications, length of stay and Karnofsky Performance Scale (KPS). Secondary outcomes were endoscopy use, perioperative treatments, tumor control and acute MRI FLAIR/T2 changes to assess for brain manipulation and retraction injury. Of 329 patients, keyhole approaches were utilized in 193(59%) patients (mean age 59±13; 30 (15.5%) had prior surgery) who underwent 213 operations; 205(96%) were skull base location. Approaches included: endoscopic endonasal (n = 74,35%), supraorbital (n = 73,34%), retromastoid (n = 38,18%), mini-pterional (n = 20,9%), suboccipital (n = 4,2%), and contralateral transfalcine (n = 4,2%). Primary outcomes: Gross total/near total (>90%) resection was achieved in 125(59%) (5% for petroclival, cavernous sinus/Meckel's cave, spheno-cavernous locations vs 77% for all other locations). Major complications included: permanent neurological worsening 12(6%), CSF leak 2(1%) meningitis 2(1%). There were no DVTs, PEs, MIs or 30-day mortality. Median LOS decreased from 3 to 2 days in the last 2 years; 94% were discharged to home with favorable 90-day KPS in 176(96%) patients. Secondary outcomes: Increased FLAIR/T2 changes were noted on POD#1/2 MRI in 36/213(17%) cases, resolving in all but 11 (5.2%). Endoscopy was used in 87/139(63%) craniotomies, facilitating additional tumor removal in 55%. Tumor progression occurred in 26(13%) patients, mean follow-up 42±36 months. Our experience suggests minimally invasive keyhole transcranial and endoscopic endonasal meningioma removal is associated with comparable resection rates and low complication rates, short hospitalizations and high 90-day performance scores in comparison to prior reports using traditional skull base approaches. Subtotal removal may be appropriate for invasive/adherent meningiomas to avoid neurological deficits and other post-operative complications, although longer follow-up is needed. With careful patient selection and requisite experience, these approaches may be considered reasonable alternatives to traditional transcranial approaches.

Validation of Two Novel and Complementary Training Platforms for Small Joint Arthroscopy

We developed 2 complementary low-fidelity models to be used to create the tool skills needed to perform small joint arthroscopy. The purpose of the study was to establish the face and construct validity of the 2 models. The "foundation model" was constructed from lemon and radish sections, and the "advanced model" was constructed from a chicken knee. Using both models, novice, intermediate, and experienced participants were asked to perform specific tasks and were timed and scored on their performance. The experienced surgeons were given a 16-item survey to rate how closely each model emulated reality to determine face validity. For the foundation model, the mean total time for the completion of tasks was 1,138 seconds for novices, 1,059 seconds for intermediates, and 631 seconds for experienced, with significant differences between the groups for time to complete 2 of the tasks. With a maximum possible score of 50 points for the correct performance of all tasks, the mean total performance score was 23 for novices, 31.8 for intermediates, and 42.2 for experienced operators. For the advanced model, the mean total time for completion was 266 seconds for novices, 147 seconds for intermediates, and 72 seconds for experienced participants. With a maximum possible score of 31 points for the correct performance of all tasks, the mean total performance score was 1.9 for novices, 15.0 for intermediates, and 24.3 for experienced participants. The average scores for the face validity surveys using a 5-point Likert scale were 4.2 and 4.5 of 5 possible points for the foundation and advanced models, respectively. Experienced operators completed the tasks more quickly and had higher performance scores than the operators in other groups. This correlation between experience and performance suggests that both models have construct validity. The face validity scores were on the upper end of the scale, suggesting that both models emulate reality for experienced operators. These novel models provide low-cost, available and valid simulations conducive to high-repetition training.

Society of General Internal Medicine Position Statement on Social Risk and Equity in Medicare's Mandatory Value-Based Payment Programs.

The Affordable Care Act (2010) and Medicare Access and CHIP Reauthorization Act (2015) ushered in a new era of Medicare value-based payment programs. Five major mandatory pay-for-performance programs have been implemented since 2012 with increasing positive and negative payment adjustments over time. A growing body of evidence indicates that these programs are inequitable and financially penalize safety-net systems and systems that care for a higher proportion of racial and ethnic minority patients. Payments from penalized systems are often redistributed to those with higher performance scores, which are predominantly better-financed, large, urban systems that serve less vulnerable patient populations - a "Reverse Robin Hood" effect. This inequity may be diminished by adjusting for social risk factors in payment policy. In this position statement, we review the literature evaluating equity across Medicare value-based payment programs, major policy reports evaluating the use of social risk data, and provide recommendations on behalf of the Society of General Internal Medicine regarding how to address social risk and unmet health-related social needs in these programs. Immediate recommendations include implementing peer grouping (stratification of healthcare systems by proportion of dual eligible Medicare/Medicaid patients served, and evaluation of performance and subsequent payment adjustments within strata) until optimal methods for accounting for social risk are defined. Short-term recommendations include using census-based, area-level indices to account for neighborhood-level social risk, and developing standardized approaches to collecting individual socioeconomic data in a robust but sensitive way. Long-term recommendations include implementing a research agenda to evaluate best practices for accounting for social risk, developing validated health equity specific measures of care, and creating policies to better integrate healthcare and social services.

Impact of background music on the performance of laparoscopy teams

BackgroundAcoustic conditions in the operating room have different impacts on surgeon’s performance. Their effects on the performance of surgical teams are not well documented. We investigated if laparoscopic teams operating under pleasant acoustic conditions would perform better than under noisy conditions.MethodsWe recruited 114 surgical residents and built 57 two-person teams. Each team was required to perform two laparoscopic tasks (object transportation and collaborative suturing) on a simulation training box under music, neutral, and noisy acoustic conditions. Data were extracted from video recordings of each performance for analysis. Task performance was measured by the duration of time to complete a task and the total number of errors, and objective performance scores. The measures were compared over the three acoustic conditions.ResultsA music environment elicited higher performance scores than a noisy environment for both the object transportation (performance score: 66.3 ± 8.6 vs. 57.6 ± 11.2; p < 0.001) and collaborative suturing tasks (78.6 ± 5.4 vs. 67.2 ± 11.1; p < 0.001). Task times in the music and noisy environments was subtracted to produce a music-noisy difference time. Pearson correlation coefficient analysis showed a significant negative relationship between the team experience score and the music-noisy difference time on the object transportation (r = − 0.246, p = 0.046) and collaborative suturing tasks (r = − 0.248, p = 0.044).ConclusionsAs to individuals, music enhances the performance of a laparoscopy team while a noisy environment worsens performance. The negative correlation between team experience and music-noisy difference time suggests that laparoscopy teams composed of experienced surgeons are less likely affected by an acoustic distraction than the noisy teams. Team resistance to acoustic distraction may lead to a new way for assessing team skills.

Erectile Dysfunction in Cirrhosis: Its Prevalence and Risk Factors

Erectile dysfunction (ED) is common in men with cirrhosis. The aim of this study was to assess the prevalence of ED and the factors associated with ED in men with cirrhosis. 400 men with cirrhosis [Child-Turcotte-Pugh (CTP) class A, 44.0%; CTP class B, 41.0%; and CTP class C, 15.0%] having high Karnofsky performance score, and living in a stable monogamous relationship with a female partner were included in the study. International Index of Erectile Function (IIEF) questionnaire, and Short-Form (36) Health Survey (SF-36) were used to assess erectile function and the health-related quality of life (HRQOL), respectively. ED was found in 289 (72.3%) patients. Patients with ED reported significantly lower SF-36 scores across all the eight domains of SF-36 (i.e., physical functioning score, role physical score, bodily pain score, general health perception score, vitality score, social functioning score, role emotional score, and mental health score); physical component summary score, and mental physical component summary score, compared with those without ED. On multivariate analysis, factors associated with ED were older age, longer duration of cirrhosis, CTP-C (vs. CTP-A), higher hepatic venous pressure gradient (HVPG), presence of generalized anxiety disorder (GAD), presence of major depression, and lower appendicular skeletal muscle index measured by dual-energy X-ray absorptiometry (DEXA ASMI). ED is common in men with cirrhosis, and men with ED have poor HRQOL compared with those without ED. Older age, longer duration of cirrhosis, CTP-C (vs. CTP-A), higher HVPG, presence of GAD, presence of major depression, and lower DEXA ASMI are associated with ED.

Making Informed Decisions to Improve Restaurant Image Using a Hybrid MADM Approach: A Case of Fast-Food Restaurants in an Island of East Malaysia

Restaurant image refers to an immediate perception that pops up in a customer’s mind when the name of a restaurant is mentioned. Therefore, it is crucial for restaurants, including fast-food restaurants (FFRs), to evaluate and sustain a positive restaurant image. However, evaluating and improving a restaurant’s image is challenging, since it counts in multiple service attributes associated with various degrees of unknown priority. Even so, the existing literature is yet to outspread the usage of an appropriate multi-attribute decision-making (MADM)-based approach to specifically evaluate the image of FFRs. Therefore, this research aimed at employing such an approach to evaluate the image of four FFRs on an island in East Malaysia, using various people, processes, and physical evidence attributes. Firstly, an initial list of FFR image attributes was elicited from the available literature. This initial list was then further validated through a two-round Delphi survey involving a panel of ten experts. A questionnaire was then designed based on the finalized attributes, and data collected from a sample of 251 respondents were analyzed using the compromised-analytical hierarchy process (C-AHP) method. The C-AHP results suggest that the strategies to improve an FFR’s image should primarily incorporate the following six attributes: hospitality, employees’ problem-solving skills, employees’ knowledge, food taste, physical cleanliness, and service response time. The FFR at the top of the ranking has the highest performance scores over these same six attributes. Surprisingly, employees’ appearance and restaurant exterior were reported as the two least important image attributes. This research is the first to demonstrate the application of a hybrid MADM-based approach to uncover the weights of FFR image attributes and rank those FFRs by computing their aggregated image scores.

Correlates and Clusters of Factors Influencing Perceived Organizational Performance of Pharmaceutical Managers in Nigeria: A Cross-Sectional Study

There is limited evidence to show how pharmaceutical managers are profiled based on managerial roles: operational, product, and strategic management level, using clustering and multivariate analyses. The constructs evaluated were:self-efficacy (SE), reward-structure (RS), policy-involvement (PE), task-environment (TE), communication-engagement (CE), and perceived organizational performance (OP). Study objectives were to investigate the profile of pharmaceutical managers based on functional roles and task constructs influencing their performance. Secondly, to assess the influence of SE, TE, PE, CE, and RS on OP of pharmaceutical managers. A literature-guided questionnaire cross-sectional study was administered by stratified random sampling to 241 managers involved in pharmaceutical marketing in Nigeria. Multivariate analyses were used to examine relationships between variables. Two-step Cluster analysis was used to explore the distinct structure of constructs. Kruskal Wallis test compared cluster groups at p≤.05. Associations existed between demographic attributes and managerial roles except for gender (p=0.085), and qualification as pharmacist or non-pharmacist (p=0.124). The regression model showed that SE, TE, and PE were significant predictors of OP. CS and RS had no significant influence on OP. Significant positive relationships were found between six constructs. Three clusters were computed with an overall median cluster score of 24.13. Strategic managers formed the dominant cluster 1 (mean=24.39) with comparatively higher value of PE than Clusters 2 (product managers) and 3 (operational managers). Higher performance scores were related to higher levels of perceived self-efficacy among managers. The study recommends need-specific interventions to address role-specific challenges affecting managers. Adopting improved communication and reward systems to improve overall performance is recommended.

Assessing the implementation of governance best practices by Latin American ports

In this paper, a semi-structured interview approach is used to assess governance and institutionalist practices promoted among successful port systems worldwide, in Latin American ports. The aim is to provide recommendations that would allow Latin American ports to reach high performance scores through governance practices, and face the challenges imposed by the COVID-19 pandemic, fostering a more resilient port ecosystem. A three-stage methodological framework, composed of six steps, is proposed. In the first stage, semi-structured interviews were carried out, with a total of 178 participants, to assess the state of implementation of governance practices in 24 Latin American ports. These practices were identified from a literature review and are promoted among successful port systems worldwide. In a second stage, the relationship between governance practices in ports and their performance was analyzed, aiming to identify those governance and institutionalist strategies that are most extensively used by the best performing ports. Finally, in a third stage, we provide recommendations that would allow Latin American ports to reach high performance scores through improved governance practices. The results indicate that digitalization and coordination among the stakeholders of the port logistics community are two of the most cited governance practices in the best performing Latin American ports. This paper contributes to the theory of port governance in Latin America, by empirically relating and identifying those governance practices which contribute to port performance.

Effect of age, cerebral infarcts, vasculopathy and haemoglobin on cognitive function, in Tanzanian children with sickle cell anaemia.

Developmental difficulties in many cognitive domains are common in children with sickle cell anaemia (SCA). Children with stroke are most affected but delayed or atypical cognitive function has been reported in children with SCA and silent infarcts (SCI), vasculopathy, and normal brain MRI. However, very few studies of cognition have been conducted in Africa, a continent with 75% of the SCA burden. We therefore investigated cognitive profiles in Tanzanian children with SCA and examined the impact of age, SCI, vasculopathy, and haemoglobin concentration (Hb). Children aged 6-16 years with and without SCA were eligible for this cross-sectional study. Cognitive assessment was performed using Raven's Matrices, assessing fluid, non-verbal intelligence and subtests from the Wechsler Intelligence Scales for Children (WISC-IV), assessing processing speed (PS), perceptual reasoning (PR), and working memory (WM) as these tests are less culture-bound. Magnetic resonance imaging (MRI) and angiography (MRA) were also completed to assess the presence of SCI and vasculopathy. Hb was collected in both SCA children and their non-SCA siblings. Seventy-three children with SCA and 71 healthy siblings (Meanages 11.9, SD=2.8 and 11.1, SD=2.9 years respectively) were recruited. Compared with healthy siblings, children with SCA had lower PS (Meandiff 7.35 points; p=.002). Older children had higher performance scores on all tests in relation to their ages. Lowest cognitive scores were observed on the PS subtest, where patients with SCI (SCI+) had lowest mean values as compared to children with no SCI (SCI-) and healthy siblings (i.e., SCI+<SCI-<healthy siblings, p=.028). On post-hoc analysis the difference was between SCI+ and healthy siblings SCI+<non-SCA siblings (p=.015); there was no difference between SCI+ and SCI- patient groups. PS was significantly lower in SCA patients with no vasculopathy as compared to healthy siblings. The mean difference from healthy siblings was -8.352 and -0.752 points for VASC- and VASC+respectively (p=.004). There was a significant positive effect of Hb on PSI (p=.001) in both patients and controls and a trend level significant positive effect of Hb on PR (p=.050) and WM (p=.051). In this Tanzanian study, cognitive performance was reduced in children with SCA with or without SCI on MRI or vasculopathy. Cognitive performance improved with increasing age. Lower Hb was associated with lower cognitive performance in both patients with SCA and their non-SCA siblings. SCI and vasculopathy do not appear to have an impact on cognitive function.

Teamwork and Adherence to Guideline on Newborn Resuscitation-Video Review of Neonatal Interdisciplinary Teams.

BackgroundLittle is known about the importance of non-technical skills for the adherence to guidelines, when teams of midwives, obstetricians, anesthesiologists, and pediatricians resuscitate and support the transition of newborns. Non-technical skills are competences underpinning successful teamwork in healthcare. These are usually referred to as leadership, situational awareness, communication, teamwork, decision making, and coping with stress and fatigue.ObjectiveBy review of videos of teams managing newborns with difficult transition, we aimed to investigate whether the level of the teams' non-technical skills was associated with the degree of adherence to guidelines for newborn resuscitation and transitional support at birth.MethodsFour expert raters independently assessed 43 real-life videos of teams managing newborns with transitional difficulties, two assessed the non-technical score and two assessed the clinical performance. Exposure was the non-technical score, obtained by the Global Assessment Of Team Performance checklist (GAOTP). GAOTP was rated on a Likert Scale 1–5 (1 = poor, 3 = average and 5 = excellent). The outcome was the clinical performance score of the team assessed according to adherence of the European Resuscitation Counsel (ERC) guideline for neonatal resuscitation and transitional support. The ERC guideline was adapted into the checklist TeamOBS-Newborn to facilitate a structured and simple performance assessment (low score 0–60, average 60–84, high 85–100). Interrater agreement was analyzed by intraclass correlation (ICC), Bland-Altman analysis, and Cohen's kappa weighted. The risk of high and low clinical performance was analyzed on the logit scale to meet the assumptions of normality and constant standard deviation.ResultsTeams with an excellent non-technical score had a relative risk 5.5 [95% confidence interval (CI) 2.4–22.5] of high clinical performance score compared to teams with average non-technical score. In addition, we found a dose response like association. The specific non-technical skills associated with the highest degree of adherence to guidelines were leadership and teamwork, coping with stress and fatigue, and communication with parents. Inter-rater agreement was high; raters assessing non-technical skills had an interclass coefficient (ICC) 0.88 (95% CI 0.79–0.94); the neonatologists assessing clinical performance had an ICC of 0.81 (95% CI 0.66–0.89).ConclusionTeams with an excellent non-technical score had five times the chance of high clinical performance compared to teams with average non-technical skills. High performance teams were characterized by good leadership and teamwork, coping with stress, and fatigue and communication with parents.