Tag: statistics

Dis Colon Rectum. 2023 May 9. doi: 10.1097/DCR.0000000000002927. Online ahead of print.

NO ABSTRACT

PMID:37163658 | DOI:10.1097/DCR.0000000000002927

Wounds. 2023 May;35(5):91-98.

ABSTRACT

INTRODUCTION: Chronic wounds are a significant problem worldwide, with substantial cost to health care systems; thus, a minimally invasive and well-tolerated treatment is attractive. Blue light has shown promise in wound healing through the principle of photobiomodulation.

OBJECTIVE: This review examines the physiological effects of blue light on tissue and the hypothesis that appropriate application of blue light in conjunction with SOC improves wound healing compared with SOC alone.

METHODS: The authors searched in PubMed, Google Scholar, and the Cochrane Library to identify literature on the mechanism of action of blue light and then examined the clinical evidence.

RESULTS: Key physiological pathways of blue light include generation of ROS and nitric oxide, resulting in promotion of angiogenesis, reduced inflammation, and direct antimicrobial effects. These reactions are seen only at low doses; in fact, higher doses may be harmful to tissue. The only primary study with statistical analyses demonstrated wound area reduction of 51% (P =.007) in blue light-irradiated wounds compared with SOC alone.

CONCLUSIONS: Blue light applied following a strict protocol is safe and shows promise in the management of chronic wounds. The current evidence is poor, however, and randomized trials are required to confirm its clinical utility.

PMID:37163654

Spine (Phila Pa 1976). 2023 May 10. doi: 10.1097/BRS.0000000000004713. Online ahead of print.

ABSTRACT

STUDY DESIGN: Systematic review and meta-analysis.

OBJECTIVE: To determine a pooled incidence rate for deep SSI, and compare available evidence for deep surgical site infection (SSI) management among instrumented spinal fusions.

SUMMARY OF BACKGROUND DATA: Deep SSI is a common complication of instrumented spinal surgery associated with patient morbidity, poorer long-term outcomes, and higher healthcare costs.

METHODS: We systematically searched Medline and Embase, and included studies with an adult patient population undergoing posterior instrumented spinal fusion of the thoracic, lumbar or sacral spine, with a reported outcome of deep SSI. The primary outcome was the incidence of deep SSI. Secondary outcomes included persistent deep SSI following initial debridement, mean number of debridements, and microbiology. Subsequent meta-analysis combined outcomes for surgical-site infection using a random-effects model and quantified heterogeneity using the χ2 test and the I2 statistic. Additionally, qualitative analysis of management strategies was reported.

RESULTS: Of 9087 potentially eligible studies, we included 54 studies (37 comparative, 17 non-comparative). The pooled SSI incidence rate was 1.5% (95% CI, 1.1% to 1.9%) based on 209,347 index procedures. Up to 25% of patients (95% CI 16.8% to 35.3%), had a persistent infection. These patients require an average of 1.4 (range: 0.8-1.9) additional debridements. Infecting organisms were commonly gram-positive and among them, staphylococcus aureus was the most frequent (46%). Qualitative analysis suggests implant retention, especially for early deep SSI management. Evidence was limited for other management strategies.

CONCLUSIONS: The pooled incidence rate of deep SSI post-thoracolumbar spinal surgery is 1.5%. The rate of recurrence and repeat debridement is at least 12%, up to 25%. Persistent infection is a significant risk, highlighting the need for standardized treatment protocols. Our review further demonstrates heterogeneity in management strategies. Large-scale prospective studies are needed to develop better evidence around deep SSI incidence and management in the instrumented thoracolumbar adult spinal fusion population.

PMID:37163651 | DOI:10.1097/BRS.0000000000004713

Autism Res. 2023 May 10. doi: 10.1002/aur.2936. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is associated with abnormal brain imaging findings, but descriptions thereof are inconsistent. The aim of the present study was to investigate brain abnormalities in young children with ASD using a combination of structural and functional brain magnetic resonance imaging (MRI). Structural and resting-state functional MRI was performed in 67 children with ASD (aged 2-7 years) and 39 age-matched typically developing (TD) controls. Voxel-based morphometry was used to evaluate differences in brain structure between groups. Topologic parameters of the functional brain network were compared by graph theoretic analysis and network connectomes were compared with network-based statistics. A support vector machine (SVM) was used to discriminate between ASD and TD groups. Results demonstrated young children with ASD had increased gray matter volumes (GMVs) in the right medial superior frontal gyrus and left fusiform gyrus compared with the TD group. The ASD group had altered subnetwork connectivity in frontal and temporal lobes and other social cognition-related brain regions. Functional connectivity in the left superior temporal gyrus and left temporal pole of the middle temporal gyrus was positively correlated with adaptability and language developmental quotient (DQ) in children with ASD. The combination of the brain structural and functional features had 86.2% accuracy in discriminating between ASD and TD. The present study shows that young children with ASD have altered GMVs and functional networks in social cognition-related brain regions, which are potential neuroimaging biomarkers for ASD.

PMID:37163546 | DOI:10.1002/aur.2936

J Perinat Med. 2023 May 8. doi: 10.1515/jpm-2022-0353. Online ahead of print.

ABSTRACT

OBJECTIVES: Analyze the diagnostic and prognostic value of the sFlt-1/PlGF ratio in pregnant women with at least one sign/symptom of suspected/diagnosed pre-eclampsia.

METHODS: This retrospective observational study included 170 pregnant women with at least one sign/symptom of pre-eclampsia, who had sFlt-1/PlGF ratio values. The following information was evaluated: pregnant women’s demographic data and clinical history; laboratory data (urine protein/creatinine ratio; sFlt-1/PlGF ratio); signs and symptoms presented; clinical outcome; fetal complications; data related to childbirth. Statistical analysis was performed by R Software Version 3.5.2.

RESULTS: Among the 170 patients, 78 presented pre-eclampsia. The median sFlt-1/PlGF ratio was significantly higher [143.1 (2.2-2,927.1)] for women who presented pre-eclampsia than for women without pre-eclampsia [33.5 (0.8-400.2)]. The negative predictive value of sFlt-1/PlGF ratio <38 was 83.9 % (95 % CI, 71.7-92.4 %) and the positive predictive value of sFlt-1/PlGF ratio >85 or 110 (for late onset pre-eclampsia) was 76.4 % (95 % CI, 66.2-84.8 %). sFlt-1/PlGF >85 or 110 was associated with pre-eclampsia clinical development, fetal complications, shorter gestational age at birth, higher number of caesarean deliveries and lower birth weight.

CONCLUSIONS: The sFlt-1/PlGF ratio, together with the standard diagnostic criteria, can be used to rule out pre-eclampsia, identify high-risk patients and predict the occurrence of adverse outcomes.

PMID:37163520 | DOI:10.1515/jpm-2022-0353

PLoS One. 2023 May 10;18(5):e0285452. doi: 10.1371/journal.pone.0285452. eCollection 2023.

ABSTRACT

Italy was the first European country to be significantly impacted by the COVID-19 pandemic. The lack of similar previous experiences and the initial uncertainty regarding the new virus resulted in an unpredictable health crisis with 243,506 total confirmed cases and 34,997 deaths between February and July 2020. Despite the panorama of precariousness and the impelling calamity, the country successfully managed many aspects of the early stages of the health and socio-economic crisis. Nevertheless, many disparities can be identified at the regional level. The study aims to determine which aspects of regional management were considered more important by the citizens regarding economic and health criteria. A survey was designed to gather responses from the population on the Italian regions’ response and provide a ranking of the regions. The 29-item online survey was provided to 352 individuals, and the collected data were analyzed using the Analytic Hierarchy Process methodology. The results show a general agreement in considering of greater relevance the healthcare policies rather than the economic countermeasures adopted by regional governments. Our analysis associated a weight of 64% to the healthcare criteria compared to the economic criteria with a weight of 36%. In addition to the results obtained from the Analytic Hierarchy Process, the sample’s composition was analyzed to provide an overall assessment of the Italian regions. To do so, we collected objective data for each region and multiplied them by the overall weight obtained for each sub-criteria. Looking at the propensity to vaccination or the belief in a relation between COVID-19 and 5G according to age and educational qualification helps understand how public opinion is structured according to cultural and anthropological differences.

PMID:37163510 | DOI:10.1371/journal.pone.0285452

PLoS One. 2023 May 10;18(5):e0284141. doi: 10.1371/journal.pone.0284141. eCollection 2023.

ABSTRACT

BACKGROUND: Breast and cervical cancers are in the top 10 most common cancers in women globally and the most common cancers in Nigerian women. The incidences have been rising steadily over the years. Involvement of men as key players in reproductive health issues has been receiving global attention especially in low and middle-income countries.

AIM: To assess male involvement in their female partners’ screening for breast and cervical cancers in Southwest, Nigeria.

METHOD: This was a community-based, cross-sectional study that employed a multi-stage sampling method to select 254 men who were married or in steady relationships in Lagos State, Southwest Nigeria. Data were collected from June to October 2018 using a semi-structured interviewer-administered questionnaire, analyzed using Epi Info version 3.5.1 and summarized with mean and standard deviation. Chi-square test was used for bivariate statistics, and the p-value of ≤0.05 was considered statistically significant. Multivariable logistic regression was used for predictor variables of male involvement in screening.

RESULTS: 29.5% of the respondents had good knowledge of breast and cervical cancers and screening and majority (85.5%) had a positive attitude towards screening. Only few, 19.3% and 15.7% had provided money for breast and cervical cancer screening respectively. Most men, 75% and 87.4% respectively had not accompanied their wife/female partner for breast and cervical cancer screening, while almost half (49.2%) and one-third (33.5%) respectively, had encouraged their female partners to screen for breast and cervical cancers. Overall, only about half, 138 (54.3%) of the men were considered ‘involved’ in their female partners’ screening for breast and cervical cancers. Male involvement was significantly associated with screening for female cancers (χ2 = 77.62, p = 0.001). Older age group (AOR = 2.64, 95% CI: 1.3-4.9), higher educational attainment (AOR = 3.51, 95% CI: 1.14-10.73), and positive attitude (AOR = 2.48, 95% CI:1.16-5.33) were found to be the predictors of male involvement.

CONCLUSION: Community-based programs for males, especially the younger and less educated, should be implemented to increase their involvement. It is also suggested that mass media messages be spread and online platforms be explored in order to increase men’s awareness and participation in female cancer screening.

PMID:37163507 | DOI:10.1371/journal.pone.0284141

PLoS One. 2023 May 10;18(5):e0283353. doi: 10.1371/journal.pone.0283353. eCollection 2023.

ABSTRACT

In 2015, the Open Science Collaboration repeated a series of 100 psychological experiments. Since a considerable part of these replications could not confirm the original effects and some of them pointed in the opposite direction, psychological research is said to lack reproducibility. Several general criticisms can explain this finding, such as the standardized use of undirected nil-null hypothesis tests, samples being too small and selective, lack of corrections for multiple testing, but also some widespread questionable research practices and incentives to publish positive results only. A selection of 57,909 articles from 12 renowned journals is processed with the JATSdecoder software to analyze the extent to which several empirical research practices in psychology have changed over the past 12 years. To identify journal- and time-specific changes, the relative use of statistics based on p-values, the number of reported p-values per paper, the relative use of confidence intervals, directed tests, power analysis, Bayesian procedures, non-standard α levels, correction procedures for multiple testing, and median sample sizes are analyzed for articles published between 2010 and 2015 and after 2015, and in more detail for every included journal and year of publication. In addition, the origin of authorships is analyzed over time. Compared to articles that were published in and before 2015, the median number of reported p-values per article has decreased from 14 to 12, whereas the median proportion of significant p-values per article remained constant at 69%. While reports of effect sizes and confidence intervals have increased, the α level is usually set to the default value of .05. The use of corrections for multiple testing has decreased. Although uncommon in each case (4% in total), directed testing is used less frequently, while Bayesian inference has become more common after 2015. The overall median estimated sample size has increased from 105 to 190.

PMID:37163505 | DOI:10.1371/journal.pone.0283353

PLoS One. 2023 May 10;18(5):e0282924. doi: 10.1371/journal.pone.0282924. eCollection 2023.

ABSTRACT

Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessment: away from traditional reliance upon animal-intensive in vivo protocols, and towards increased application of in silico (or computational) predictive toxicology. With QSAR central amongst techniques applied in this area, the emergence of algorithms trained through machine learning with the objective of toxicity estimation has, quite naturally, arisen. On account of the pattern-recognition capabilities of the underlying methods, the statistical power of the ensuing models is potentially considerable-appropriate for the handling even of vast, heterogeneous datasets. However, such potency comes at a price: this manifesting as the general practical deficits observed with respect to the reproducibility, interpretability and generalisability of the resulting tools. Unsurprisingly, these elements have served to hinder broader uptake (most notably within a regulatory setting). Areas of uncertainty liable to accompany (and hence detract from applicability of) toxicological QSAR have previously been highlighted, accompanied by the forwarding of suggestions for “best practice” aimed at mitigation of their influence. However, the scope of such exercises has remained limited to “classical” QSAR-that conducted through use of linear regression and related techniques, with the adoption of comparatively few features or descriptors. Accordingly, the intention of this study has been to extend the remit of best practice guidance, so as to address concerns specific to employment of machine learning within the field. In doing so, the impact of strategies aimed at enhancing the transparency (feature importance, feature reduction), generalisability (cross-validation) and predictive power (hyperparameter optimisation) of algorithms, trained upon real toxicity data through six common learning approaches, is evaluated.

PMID:37163504 | DOI:10.1371/journal.pone.0282924

PLoS One. 2023 May 10;18(5):e0285277. doi: 10.1371/journal.pone.0285277. eCollection 2023.

ABSTRACT

By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model. The posterior distributions for the unknown parameters are derived, and the Markov chain Monte Carlo sampling algorithms are also given. The proposed method is illustrated by three simulation examples and a real dataset.

PMID:37163496 | DOI:10.1371/journal.pone.0285277