Category: Nevin Manimala

Int Braz J Urol. 2024 Mar 3;50. doi: 10.1590/S1677-5538.IBJU.2024.9909. Online ahead of print.

ABSTRACT

INTRODUCTION: The superiority of the functional results of robot-assisted radical prostatectomyis still controversial. Despite this, it is known that minimally invasive surgery obtains better results when analyzing blood loss, blood transfusion and length of stay, for example. Several studies have analyzed the impact of the resident physician’s involvement on the results of urological surgeries. The simple learning curve for robot-assisted radical prostate surgery is estimated to be around 10 to 12 cases. Learning curve data for robotic surgeons is heterogeneous, making it difficult to analyze. Rare studies compare the results of a radical prostatectomy of an inexperienced surgeon starting his training in open surgery, with the results of the same surgeon, a few years later, starting training in robotic surgery.

OBJECTIVE: to analyze the results of open radical prostatectomy surgeries (ORP) performed by urology residents, comparing them to the results of robot-assisted radical prostatectomy (RARP), performed by these same surgeons, after completing their training in urology.

MATERIALS AND METHODS: a retrospective analysis of the cases of only 3 surgeons was performed. 50 patients underwent ORP (group A). The surgeons who operated on the ORP patients were in the 3rd and final year of the urology residency program and beginners in ORP surgery, but with at least 4 years of experience in open surgery. The same surgeons, already trained urologists, began their training in robotic surgery and performed 56 RARP surgeries (group B). For the comparative analysis, data were collected on age, number of lymph nodes removed, surgery time, hospitalization time, drain volume, drain permanence time, indwelling bladdercateter (IBC) permanence time, positive surgical margin, biochemical recurrence, risk classification (ISUP), intra and postoperative complications, urinary incontinence (UI) and erectile dysfunction (ED). The console used was the Da Vinci Si, from Intuitive®. For statistical analysis, the Shapiro-Wilk test verified that the data did not follow normality, the Levene test guaranteed homogeneity, and the Mann-Whitney test performed the comparative analysis of the quantitative data. For the analysis of qualitative data, the Chi-square test was used for nominal variables and the Mann-Whitney U test for ordinal variables. Additionally, the Friedman test analyzed whether there was an improvement in the perception of UI or ED over the months, for each group individually (without comparing them), and the post-hoc Durbin-Conover test, for the results with statistically significant difference. We used a p-value < 0.05, and the Jamovi® program (Version 2.0).

RESULTS: there was no statistically significant difference between the groups for age, number of lymph nodes removed, positive surgical margin, biochemical recurrence, risk classification and urinary incontinence. Additionally, we observed that the surgical time was longer in group B. On the other hand, the length of stay, drain volume, drain time, IBC time, complication rate and levels of erectile dysfunction in the third and sixth months were higher in group A, when compared to group B. We also observed that there was no evolutionary improvement in ED over the months in both groups, and that there was a perception of improvement in UI from the 1st to the 3rd month in group A, and from the 1st to the 6th month, and from the 3rd to the 12th month, in group B.

CONCLUSION: the learning curve of RARP is equivalent to the curve of ORP. In general, the results for the robotic group were better, however, the functional results were similar between the groups, with a slight tendency of advantage for the robotic arm.

PMID:38446904 | DOI:10.1590/S1677-5538.IBJU.2024.9909

J Addict Med. 2024 Mar 6. doi: 10.1097/ADM.0000000000001282. Online ahead of print.

ABSTRACT

BACKGROUND: Few studies have examined illness models among people with addiction. We investigated illness models and their associations with demographics and treatment beliefs among patients receiving methadone treatment for opioid use disorder.

METHODS: From January 2019 to February 2020, patients receiving methadone treatment at outpatient opioid treatment programs provided demographics and rated using 1 to 7 Likert-type scales agreement with addiction illness models (brain disease model, chronic medical condition model [CMCM], and no explanation [NEM]) and treatment beliefs. Pairwise comparisons and multivariate regressions were used to examine associations between illness models, demographics, and treatment beliefs. Statistical significance was set at P < 0.05.

RESULTS: A total of 450 patients participated in the study. Forty percent self-identified as female, 13% as Hispanic, and 78% as White; mean age was 38.5 years. Brain disease model was the most frequently endorsed illness model (46.2%), followed by CMCM (41.7%) and NEM (21.9%). In multivariate analyses, agreement with brain disease model was significantly positively associated with beliefs that methadone treatment would be effective, counseling is important, and methadone is lifesaving, whereas agreement with CMCM was significantly positively associated with beliefs that methadone treatment would be effective, counseling is important, 12-step is the best treatment, taking methadone daily is important, and methadone is lifesaving. In multivariate analyses, agreement with NEM was negatively significantly associated with beliefs that methadone would be effective, counseling is important, taking methadone daily is important, and methadone is lifesaving.

DISCUSSION: Many patients in methadone treatment endorsed medicalized addiction models. Agreement with addiction illness models appear to be related to treatment beliefs.

PMID:38446859 | DOI:10.1097/ADM.0000000000001282

PLoS One. 2024 Mar 6;19(3):e0298957. doi: 10.1371/journal.pone.0298957. eCollection 2024.

ABSTRACT

The lifestyle of spinosaurid dinosaurs has been a topic of lively debate ever since the unveiling of important new skeletal parts for Spinosaurus aegyptiacus in 2014 and 2020. Disparate lifestyles for this taxon have been proposed in the literature; some have argued that it was semiaquatic to varying degrees, hunting fish from the margins of water bodies, or perhaps while wading or swimming on the surface; others suggest that it was a fully aquatic underwater pursuit predator. The various proposals are based on equally disparate lines of evidence. A recent study by Fabbri and coworkers sought to resolve this matter by applying the statistical method of phylogenetic flexible discriminant analysis to femur and rib bone diameters and a bone microanatomy metric called global bone compactness. From their statistical analyses of datasets based on a wide range of extant and extinct taxa, they concluded that two spinosaurid dinosaurs (S. aegyptiacus, Baryonyx walkeri) were fully submerged “subaqueous foragers,” whereas a third spinosaurid (Suchomimus tenerensis) remained a terrestrial predator. We performed a thorough reexamination of the datasets, analyses, and methodological assumptions on which those conclusions were based, which reveals substantial problems in each of these areas. In the datasets of exemplar taxa, we found unsupported categorization of taxon lifestyle, inconsistent inclusion and exclusion of taxa, and inappropriate choice of taxa and independent variables. We also explored the effects of uncontrolled sources of variation in estimates of bone compactness that arise from biological factors and measurement error. We found that the ability to draw quantitative conclusions is limited when taxa are represented by single data points with potentially large intrinsic variability. The results of our analysis of the statistical method show that it has low accuracy when applied to these datasets and that the data distributions do not meet fundamental assumptions of the method. These findings not only invalidate the conclusions of the particular analysis of Fabbri et al. but also have important implications for future quantitative uses of bone compactness and discriminant analysis in paleontology.

PMID:38446841 | DOI:10.1371/journal.pone.0298957

PLOS Glob Public Health. 2024 Mar 6;4(3):e0002992. doi: 10.1371/journal.pgph.0002992. eCollection 2024.

ABSTRACT

Although postnatal care services form a critical component of the cascade of care in maternal, newborn, and child health the uptake of these services has remained low worldwide. This study explored and prioritised the strategies for optimising the uptake of postnatal care (PNC) services in Malawi. A qualitative descriptive study followed by nominal group techniques was conducted at three health facilities in Malawi from July to December 2020 and in October 2021. We conducted focus group discussions among postnatal mothers, fathers, healthcare workers, elderly women, and grandmothers. We conducted in-depth interviews with midwives and key health managers. Nominal group techniques were used to prioritise the main strategies for the provision of PNC. The demand strategies include appointment date reminders, provision of free health passport books, community awareness campaigns, and involvement of men in the services. The supply strategies included training health providers, improving clinic operations: task-shifting and hours of operation, having infrastructure for the services, and linkage to other services. Having services delivered near end-user residences was a crosscutting strategy. Refresher training and improvement in the clinic operations especially on hours of operation, appointment date reminders, and linkage to care were the prioritised strategies. There is a need to use acceptable and contextualised strategies to optimise the uptake and delivery of postnatal care services. Educating the healthcare workers and the community on postnatal services is key to increasing the demand and supply of the services.

PMID:38446818 | DOI:10.1371/journal.pgph.0002992

PLoS One. 2024 Mar 6;19(3):e0298597. doi: 10.1371/journal.pone.0298597. eCollection 2024.

ABSTRACT

Gum bleeding is a common dental problem, and numerous patients seek health-related information on this topic online. The YouTube website is a popular resource for people searching for medical information. To our knowledge, no recent study has evaluated content related to bleeding gums on YouTube™. Therefore, this study aimed to conduct a quantitative and qualitative analysis of YouTube videos related to bleeding gums. A search was performed on YouTube using the keyword “bleeding gums” from Google Trends. Of the first 200 results, 107 videos met the inclusion criteria. The descriptive statistics for the videos included the time since upload, the video length, and the number of likes, views, comments, subscribers, and viewing rates. The global quality score (GQS), usefulness score, and DISCERN were used to evaluate the video quality. Statistical analysis was performed using the Kruskal-Wallis test, Mann-Whitney test, and Spearman correlation analysis. The majority (n = 69, 64.48%) of the videos observed were uploaded by hospitals/clinics and dentists/specialists. The highest coverage was for symptoms (95.33%). Only 14.02% of the videos were classified as “good”. The average video length of the videos rated as “good” was significantly longer than the other groups (p <0.05), and the average viewing rate of the videos rated as “poor” (63,943.68%) was substantially higher than the other groups (p <0.05). YouTube videos on bleeding gums were of moderate quality, but their content was incomplete and unreliable. Incorrect and inadequate content can significantly influence patients’ attitudes and medical decisions. Effort needs to be expended by dental professionals, organizations, and the YouTube platform to ensure that YouTube can serve as a reliable source of information on bleeding gums.

PMID:38446816 | DOI:10.1371/journal.pone.0298597

PLoS One. 2024 Mar 6;19(3):e0295814. doi: 10.1371/journal.pone.0295814. eCollection 2024.

ABSTRACT

BACKGROUND: The prospective cohort study PROTECT is the largest study in pediatric ulcerative colitis (UC) with standardized treatments, providing valuable data for predicting clinical outcomes. PROTECT and previous studies have identified characteristics associated with clinical outcomes. In this study, we aimed to compare predictive modeling between Bayesian analysis including machine learning and frequentist analysis.

METHODS: The key outcomes for this analysis were week 4, 12 and 52 corticosteroid (CS)-free remission following standardized treatment from diagnosis. We developed predictive modeling with multivariable Bayesian logistic regression (BLR), Bayesian additive regression trees (BART) and frequentist logistic regression (FLR). The effect estimate of each risk factor was estimated and compared between the BLR and FLR models. The predictive performance of the models was assessed including area under curve (AUC) of the receiver operating characteristic (ROC) curve. Ten-fold cross-validation was performed for internal validation of the models. The estimation contained 95% credible (or confidence) interval (CI).

RESULTS: The statistically significant associations between the risk factors and early or late outcomes were consistent between all BLR and FLR models. The model performance was similar while BLR and BART models had narrower credible intervals of AUCs. To predict week 4 CS-free remission, the BLR model had AUC of 0.69 (95% CI 0.67-0.70), the BART model had AUC of 0.70 (0.67-0.72), and the FLR had AUC of 0.70 (0.65-0.76). To predict week 12 CS-free remission, the BLR model had AUC of 0.78 (0.77-0.79), the BART model had AUC of 0.78 (0.77-0.79), and the FLR model had AUC of 0.79 (0.74-0.83). To predict week 52 CS-free remission, the BLR model had AUC of 0.69 (0.68-0.70), the BART model had AUC of 0.69 (0.67-0.70), and the FLR model had AUC of 0.69 (0.64-0.74). The BART model identified nonlinear associations.

CONCLUSIONS: BLR and BART models had intuitive interpretation on interval estimation, better precision in estimating the AUC and can be alternatives for predicting clinical outcomes in pediatric patients with UC. BART model can estimate nonlinear nonparametric association.

PMID:38446811 | DOI:10.1371/journal.pone.0295814

PLoS One. 2024 Mar 6;19(3):e0299359. doi: 10.1371/journal.pone.0299359. eCollection 2024.

ABSTRACT

BACKGROUND: Public health expenditure is one of the fastest-growing spending items in EU member states. As the population ages and wealth increases, governments allocate more resources to their health systems. In view of this, the aim of this study is to identify the key determinants of public health expenditure in the EU member states.

METHODS: This study is based on macro-level EU panel data covering the period from 2000 to 2018. The association between explanatory variables and public health expenditure is analyzed by applying both static and dynamic econometric modeling.

RESULTS: Although GDP and out-of-pocket health expenditure are identified as the key drivers of public health expenditure, there are other variables, such as health system characteristics, with a statistically significant association with expenditure. Other variables, such as election year and the level of public debt, result to exert only a modest influence on the level of public health expenditure. Results also indicate that the aging of the population, political ideologies of governments and citizens’ expectations, appear to be statistically insignificant.

CONCLUSION: Since increases in public health expenditure in EU member states are mainly triggered by GDP increases, it is expected that differences in PHE per capita across member states will persist and, consequently, making it more difficult to attain the health equity sustainable development goal. Thus, measures to reduce EU economic inequalities, will ultimately result in reducing disparities in public health expenditures across member states.

PMID:38446804 | DOI:10.1371/journal.pone.0299359

PLoS One. 2024 Mar 6;19(3):e0299046. doi: 10.1371/journal.pone.0299046. eCollection 2024.

ABSTRACT

The agglomeration effect significantly influences firms’ site selection. Manufacturing firms often exhibit intricate spatial co-location patterns that are indicative of agglomerations due to their reliance on material input and product output across various subdivisions of manufacture. In this study, we present an analytical approach employing the Q statistic and additive color mixing visualization to assess co-location patterns of manufacturing firms. We identified frequent pairs and triplets of manufacturing divisions, mapping them to reveal distinct categories: labor-intensive clusters, upstream/downstream industrial chains, and technology-spillover clusters. These agglomeration categories concentrate in different regions of the city. Policy implications are proposed to promote the upgrade of labor-intensive divisions, enhance the operational efficiency of upstream/downstream industrial chains, and reinforce the spillover effects of technology-intensive divisions.

PMID:38446799 | DOI:10.1371/journal.pone.0299046

J Med Internet Res. 2024 Mar 6;26:e48026. doi: 10.2196/48026.

ABSTRACT

BACKGROUND: Social media addiction (SMA) caused by excessive dependence on social media is becoming a global problem. At present, most of the SMA studies recruit college students as research participants, with very few studies involving workers and other age groups, especially in China.

OBJECTIVE: This study aims to investigate the current status of SMA among Chinese workers and analyze its influencing factors.

METHODS: From November 1, 2022, to January 30, 2023, we conducted an anonymous web-based questionnaire survey in mainland China, and a total of 5176 participants completed the questionnaire. The questionnaire included the Social Networking Service Addiction Scale, Maslach Burnout Inventory-General Survey scale, Mindful Attention Awareness Scale, as well as questionnaires regarding participants’ social media use habits and demographic information.

RESULTS: Through strict screening, 3468 valid questionnaires were included in this study. The main findings of this study revealed the following: the average SMA score of workers was higher (mean 53.19, SD 12.04), and some of them (393/3468, 11.33%) relied heavily on social media; there were statistically significant differences in SMA scores among workers in different industries (F_14,3453=3.98; P<.001); single workers (t₃₁₀₆=8.6; P<.001) and workers in a relationship (t₂₇₄₉=5.67; P<.001) had higher SMA scores than married workers, but some married workers (214/3468, 6.17%) were highly dependent on social media; the level of SMA among female workers was higher than that of male workers (t₃₄₆₆=3.65; P<.001), and the SMA score of workers negatively correlated with age (r=-0.22; P<.001) and positively correlated with education level (r=0.12; P<.001); the frequency of using social media for entertainment during work (r=0.33; P<.001) and the frequency of staying up late using social media (r=0.14; P<.001) were positively correlated with the level of SMA in workers; and the level of SMA in workers was significantly positively correlated with their level of burnout (r=0.35; P<.001), whereas it was significantly negatively correlated with their level of mindfulness (r=-0.55; P<.001).

CONCLUSIONS: The results of this study suggest that SMA among Chinese workers is relatively serious and that the SMA problem among workers requires more attention from society and academia. In particular, female workers, young workers, unmarried workers, highly educated workers, workers with bad social media habits, workers with high levels of job burnout, and workers with low levels of mindfulness were highly dependent on social media. In addition, occupation is an important influencing factor in SMA. Thus, the government should strengthen the supervision of social media companies. Medical institutions should provide health education on SMA and offer intervention programs for those addicted to social media. Workers should cultivate healthy habits while using social media.

PMID:38446542 | DOI:10.2196/48026

JMIR Med Inform. 2024 Mar 6;12:e51560. doi: 10.2196/51560.

ABSTRACT

BACKGROUND: Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data.

OBJECTIVE: This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework.

METHODS: A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English.

RESULTS: A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions.

CONCLUSIONS: The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

PMID:38446534 | DOI:10.2196/51560