Tool For Assessing Risk Of Bias Research Articles

Comparison of Brain Function and Structure in Patients with Major Depression: A Systematic Review and Meta-Analysis of MRI-Based Data.

Depression is a common mental illness worldwide. Neuroimaging techniques, such as magnetic resonance imaging and functional magnetic resonance imaging, play an essential role in diagnosing and evaluating depression. This study is based on magnetic resonance imaging (MRI)-related research to explore the comparison of brain function and structure between patients with severe depression and normal individuals, and to conduct meta-analysis. We conducted searches in various databases such as PubMed, Web of Science, Embase, and Cochrane Library to obtain research data on comparing brain function and structure between patients with severe depression and healthy individuals. The search keywords included "Major Depressive Disorder", "Brain Function", "Brain Structure", "Depression", "MRI", and "Magnetic Resonance". The quality assessment was conducted using the bias risk assessment tool recommended by the Cochrane Collaborative Network. Literature was screened following the predetermined inclusion and exclusion criteria, and Anisotropic Effect-Size Seed-Based Differential Mapping (AES-SDM) was used for systematic meta-analysis. Regression analysis was performed on age, gender, disease duration, years of education, and treatment status. After a thorough screening process, 10 documents were selected for subsequent analysis. These studies consisted of 477 study subjects, including 231 depression patients and 246 healthy individuals. The proportion of women was 36%-75%, and the disease duration was 3-60 months. The patients in 4 documents had first attacks, and the patients in the other 6 documents had multiple attacks. The baseline conditions of the 10 included documents were consistent and comparable. None of the studies reported blinding methods, and none of the results had incomplete data. The Regional homogeneity (ReHo) levels in the left precuneus (BA7), lentiform nucleus (BA48), and left prefrontal lobe (BA32) were significantly increased in the depression group, with voxel numbers of 358, 116, and 181, respectively. Conversely, the left postcentral gyrus (BA4), left cerebellar area (hemispheric lobule I, IV/V, lingual gyrus, fusiform gyrus), left fusiform gyrus (BA30), and right cingulate gyrus (BA23) were significantly reduced, with voxel numbers of 17, 50, and 124, respectively. Furthermore, regression analysis showed that gender, age, disease duration, years of education, and disease severity were potential influencing factors, and the disease duration demonstrated the most significant impact on the left cingulate gyrus (SDM = 2.777). There are significant differences in brain function and structure between patients with major depression and healthy individuals. Furthermore, our findings reveal a substantial correlation between the severity of depressive symptoms and brain function and structure indicators. These findings provide novel research directions and ideas for the diagnosis and treatment of depression.

Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis.

Urinary tract infections (UTIs) have been one of the most common bacterial infections in clinical practice worldwide. Artificial intelligence (AI) and machine learning (ML) based algorithms have been increasingly applied in UTI case identification and prediction. However, the overall performance of AI/ML algorithms in identifying and predicting UTI has not been evaluated. The purpose of this paper is to quantitatively evaluate the application value of AI/ML in identifying and predicting UTI cases. MEDLINE, EMBASE, Web of Science, and PubMed databases were systematically searched for articles published up to December 31, 2023. Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) and Prediction Model Risk of Bias Assessment Tool (PROBAST) were used to assess the risk of bias. Study characteristics and detailed algorithm information were extracted. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were synthesized using a bivariate mix-effects model. Meta-regression and subgroup analysis were conducted to test the source of heterogeneity. In total, 11 studies with 14 AI/ML models were included in the final meta-analysis. The overall pooled AUC was 0.89 (95%CI 0.86-0.92). Additionally, the pooled Sen, Spe, PLR, NLR, and DOR were 0.78 (95%CI 0.71-0.84), 0.89 (95%CI 0.83-0.93), 6.99 (95%CI 4.38-11.14), 0.25 (95%CI 0.18-0.34) and 28.07 (95%CI 14.27-55.20), respectively. The results of meta-regression suggested that reference standard definitions might be the source of heterogeneity. AI/ML algorithms appear to be promising to help clinicians detect and identify patients at high risk of UTIs. However, further studies are demanded to evaluate the application value of AI/ML more thoroughly.

Utility of Machine Learning, Natural Language Processing, and Artificial Intelligence in Predicting Hospital Readmissions After Orthopaedic Surgery: A Systematic Review and Meta-Analysis.

Numerous applications and strategies have been utilized to help assess the trends and patterns of readmissions after orthopaedic surgery in an attempt to extrapolate possible risk factors and causative agents. The aim of this work is to systematically summarize the available literature on the extent to which natural language processing, machine learning, and artificial intelligence (AI) can help improve the predictability of hospital readmissions after orthopaedic and spine surgeries. This is a systematic review and meta-analysis. PubMed, Embase and Google Scholar were searched, up until August 30, 2023, for studies that explore the use of AI, natural language processing, and machine learning tools for the prediction of readmission rates after orthopedic procedures. Data regarding surgery type, patient population, readmission outcomes, advanced models utilized, comparison methods, predictor sets, the inclusion of perioperative predictors, validation method, size of training and testing sample, accuracy, and receiver operating characteristics (C-statistic), among other factors, were extracted and assessed. A total of 26 studies were included in our final dataset. The overall summary C-statistic showed a mean of 0.71 across all models, indicating a reasonable level of predictiveness. A total of 15 articles (57%) were attributed to the spine, making it the most commonly explored orthopaedic field in our study. When comparing accuracy of prediction models between different fields, models predicting readmissions after hip/knee arthroplasty procedures had a higher prediction accuracy (mean C-statistic = 0.79) than spine (mean C-statistic = 0.7) and shoulder (mean C-statistic = 0.67). In addition, models that used single institution data, and those that included intraoperative and/or postoperative outcomes, had a higher mean C-statistic than those utilizing other data sources, and that include only preoperative predictors. According to the Prediction model Risk of Bias Assessment Tool, the majority of the articles in our study had a high risk of bias. AI tools perform reasonably well in predicting readmissions after orthopaedic procedures. Future work should focus on standardizing study methodologies and designs, and improving the data analysis process, in an attempt to produce more reliable and tangible results. Level III. See Instructions for Authors for a complete description of levels of evidence.

Diagnostic accuracy of digital breast tomosynthesis and digital mammography in women with dense or non-dense breast tissue: A systematic review and meta-analysis.

Despite its excellent screening effectiveness and sensitivity for breast cancer (BC), digital breast tomosynthesis (DBT) is controversial due to its high radiation exposure and long reading time. This study examines the diagnostic accuracy of DBT and digital mammography (DM) for BC screening and diagnosis in women with dense or non-dense breast tissue. PRISMA-compliant searches were performed on Medline, Embase, PubMed, Web of Science, and the Cochrane databases for articles comparing DBT and DM for BC screening until March 2023. Meta-analysis was performed using RevMan sofware, and the Cochrane Risk of Bias Assessment Tool was employed to assess study quality. This meta-analysis included 11 trials with a total of 2,124,018 individuals. Screening with DBT resulted in a greater cancer detection rate, as demonstrated by a risk ratio (RR) of 1.27 (95% confidence interval (95% CI): 1.14-1.41). Digital breast tomosynthesis also had a reduced recall rate, with a RR of 0.88 (95% CI: 0.78-0.99), higher sensitivity and specificity values (pooled sensitivity of 0.91 (95% CI: 0.59-0.99)) and pooled specificity of 0.90 (95% CI: 0.42-1.0)) than DM (pooled sensitivity of 0.86 (95% CI: 0.52-1.0) and pooled specificity of 0.81 (95% CI: 0.12-1.0)). All acquired data exhibited reliability, lack of bias and statistical significance (p < 0.05). Digital breast tomosynthesis is a more effective screening and diagnostic assessment tool for women with dense or non-dense breasts than DM in terms of incremental cancer detection, sensitivity and recall rate.

The Efficacy of Curcumin Application to Melanoma in Mice: A Systematic Review and Meta-analysis.

Melanoma is a skin tumor that poses a serious threat to human health. Our study explores the effectiveness and safety of curcumin in the treatment of melanoma based on animal models, and providing evidence-based medical evidence for curcumin in the treatment of malignant melanoma. The study collected all randomized controlled trial data from the establishment of the database to October 2023 of curcumin for the treatment of melanoma in mice by searching PubMed, Embase, and the Cochrane Library. According to inclusion and exclusion criteria, data were extracted and quality assessment of included studies was performed by using the SYRCLE (Systematic Review Center for Laboratory animal Experimentation) animal experiment bias risk assessment tool. RevMan 5.4 and Stata 15.1 software were used for meta-analysis. Eighteen randomized controlled trials were included in this study with a total of 185 mouse models, including 93 mice in the experimental group and 92 in the control group. The results of meta-analysis showed that the IC50 (inhibitory concentrations of 50%) in the experimental group is lower than that of the control group [standardized mean difference (SMD) = -4.68, 95% confidence interval (CI) (-7.30, -2.06), P < 0.01]; the tumor volume is significantly smaller than the control group [SMD = -3.10, 95% CI (-4.45, -1.75), P < 0.01]; the tumor weight is smaller than the control group [SMD = -3.01, 95% CI (-4.81, -1.21), P < 0.01]. However, there was no significant statistical difference in the apoptosis rate between the experimental group and the control group [SMD = 2.27, 95% CI (-1.39, 5.92), P < 0.01]. Based on animal models for meta-analysis, curcumin can inhibit the growth and proliferation of melanoma in mice. Melanoma may be an effective method for treating melanoma. However, this result still requires further in-depth research.

Mesenchymal Stem Cells Injection Is More Effective Than Hyaluronic Acid Injection in the Treatment of Knee Osteoarthritis With Similar Safety: A Systematic Review and Meta-Analysis

PurposeTo evaluate the Efficacy and safety of intra-articular injection of mesenchymal stem cells (MSCs) versus Hyaluronic acid (HA) in the treatment of knee osteoarthritis (KOA). MethodsEligible randomized controlled trials (RCTs) were identified through a search of Pubmed, Embase, the Cochrane Library, Web of science, SinoMed and CNKI databases from inception to March 2024. For meta-analysis, data on clinical outcomes were measured using a visual analog scale (VAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and data on cartilage repair were measured using the Whole-Organ Magnetic Resonance Imaging Score (WORMS); data on safety was evaluated by the incidence of adverse events. Two researchers independently read the included literatures, extracted data and evaluated the quality, and used Cochrane risk bias assessment tool for bias risk assessment, and RevMan5.3 software for Meta-analysis. Result：Ten RCTs involving 818 patients with KOA ranging from I-Ⅲ Kellgren – Lawrence grading scale were included in this meta-analysis. Meta results showed that compared with the HA control group, at 12months, the WOMAC total score [MD=-10.22, 95% CI (-14.86∼-5.59), P<0.0001, Z=4.32];VAS score[MD=-1.31, 95% CI (-1.90∼-0.73), P<0.0001, Z=4.40]; WORMS score [MD=-26.01,95% CI (-31.88∼-20.14),P<0.001,Z=8.69]of MSCs group all decreased significantly (P<0.05), and reached the minimum clinically important differences (MCID). Furthermore, there was no significant difference in the incidence of adverse events (RR=1.54, 95% CI= 0.85 ∼ 2.79, P=0.16, I2=0) between the two groups (P >0.05). ConclusionCompared to HA, intra-articular injection of MSCs therapy appears to effectively alleviate joint pain, improve clinical function of KOA patients. These benefits are observed to last for at least 12 months without an increase in adverse events. Due to limited, varied, and lacking MCID results in existing literature, further research is needed. Level of EvidenceLevel I, Meta-analysis of Level I studies.

The efficacy and safety of anti-vascular endothelial growth factor combined with Ahmed glaucoma valve implantation in the treatment of neovascular glaucoma: a systematic review and meta-analysis.

The intraocular injections of anti-vascular endothelial growth factor (anti-VEGF) demonstrates significant efficacy in inhibiting the formation of ocular neovascularization in neovascular glaucoma (NVG). Ahmed glaucoma valve implantation (AGVI) is extensively employed for the management of diverse glaucoma types. To further evaluate the efficacy and safety of anti-VEGF combined with AGVI in the treatment of neovascular glaucoma. A thorough search for randomized controlled trials (RCTs) was conducted across eight databases: PubMed, EMBASE, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang, SinoMed, and VIP. The search period was set from the inception of each database until March 2, 2024, to identify RCTs investigating the effectiveness and safety of combining AGVI with anti-VEGF therapy for NVG. We used the Cochrane Risk of Bias Assessment Tool to evaluate the quality of the literature and performed statistical analysis using Stata 15.0 software. Fourteen RCTs were included in this study. Compared with AGVI alone, the combination of anti-VEGF drugs and AGVI can reduce postoperative intraocular pressure (IOP) at 1 week [WMD = -4.03, 95% CI (-5.73, -2.34), p < 0.001], 1 month [WMD = -5.39, 95% CI (-7.05, -3.74), p < 0.001], 3 months [WMD = -6.59, 95% CI (-7.85, -5.32), p < 0.001], 6 months [WMD = -4.99, 95% CI (-9.56, -0.43), p = 0.032], and more than 12 months [WMD = -3.86, 95% CI (-6.82, -0.90), p = 0.011], with a higher Effective rate [RR = 1.27, 95% CI (1.18, 1.37), p < 0.001], decreased incidence of postoperative hyphema [RR = 0.24, 95% CI (0.15, 0.39), p < 0.001], reduced use of postoperative antiglaucoma medications [WMD = -0.48, 95% CI (-0.61, -0.35), p < 0.001], and decreased aqueous humor VEGF levels [SMD = -2.84, 95% CI (-4.37, -1.31), p < 0.001]. In comparison to AGVI alone, the combination of AGVI with anti-VEGF therapy has better effects in reducing IOP at various time intervals, diminishing postoperative antiglaucoma medication requirements and reducing aqueous humor VEGF levels. Furthermore, it effectively minimizes the incidence of postoperative hyphema. Nevertheless, due to the variability in the quality of the trials included, further high-quality experiments will be required in the future to substantiate this conclusion. PROSPERO, identifier CRD42024519862, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024519862.

The Effect of Polymyxin B-Immobilized Fiber Column Hemoperfusion for Sepsis: A Systemic Review and Meta-Analysis

Purpose: The objective of this study was to evaluate the effect of Polymyxin B hemoperfusion (PMX-HP) on patients with sepsis.Methods: A systematic review and meta-analysis was performed using relevant articles retrieved from 3 databases (PubMed, Cochrane Library, EMBASE). Randomized studies from 1 January 1999 to 28 February 2022 were examined to determine the clinical results of PMX-HP. A meta-analysis was carried out using the random-effects method, meta-regression with clinical variables, and assessment of risk of bias (ROB) tool (Cochrane ROB assessment tool). Mortality was evaluated within 60 days of hospitalization (in-hospital death 28-day, 30-day, and 60-day mortality) and predictors associated with mortality were determined using meta-regression.Results: There were 11 randomized studies with 548 patients included in the meta-analysis. The pooled mortality was 35% (95% CI, 27%-42%, 95% CI 0.53-0.96). Further subgroup analysis was performed according to the duration of PMX-HP. An extension of PMX-HP treatment beyond 2 hours (pooled mortality, 43%; 95% CI, 9%-76%) compared with a 2-hour session (pooled mortality, 33%. 95% CI, 27%-38%) showed an increase in mortality rates. However, this was not statistically significant. Univariate meta-regression showed that patient’s age, the acute physiology and chronic health evaluation score, and the sequential organ failure assessment score did not significantly impact mortality.Conclusion: While PMX-HP is valuable in the management of septic shock, treatment duration should be based on careful assessment of the patient's condition, the risks and benefits of prolonged therapy, and the overall treatment strategy including antimicrobial management and source control.

An Exploration of Unique Mental Health Challenges and Resilience Factors within the LGBTQ+ Community: A Systematic Review

Mental health challenges in the LGBTQ+ community pressing issues as it impact the overall health status. Despite the concerns raised on the discrimination, bias and stereotypes, new challenges arise, implicating challenges to the health sector. The present review of evidence focuses on the resilience factors and the unique mental health challenges faced by members of the LGBTQ+ community. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) were used to identify, select, appraise and synthesize evidence in the present review, whereas the Sample, phenomenon of Interest, Design, Evaluation and Research type (SPIDER) protocol guided the selection of eligible studies to be reviewed. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to rate the certainty of evidence about the unique mental health challenges and resilience factors within the lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQ+) community. Initial article search was limited to English publications and performed on electronic databases, including Google Scholar, ProQuest, PubMed, and Web of Science. The Cochrane risk of bias assessment tool was used to examine the risk of bias in the individual studies. Risk of bias assessment was performed on the domains, including due to the randomization process, bias due to deviations from intended interventions, bias due to missing outcome data, bias in the measurement of the outcomes, bias in the selection of the reported result, and the overall risk of bias, reflecting the overall risk of bias of the included studies. A qualitative synthesis of results was considered in the present systematic review. The evidence was contrasted and compared against the existing knowledge and theoretical orientation. The present review found that poor dissemination of interventions and implementations are unique mental health challenges among the LGBTQ+ community. The poor implementation and dissemination of interventions substantiate psychological distress, anxiety, depression, and suicidal thoughts and deteriorates quality of life. The web or internet-based implementation of cognitive behavioral therapy emerged as effective resilience factor in the management of mental conditions among members of the LGBTQ+ community. Keywords: LGBTQ+, mental health, treatment, cognitive behavioral therapy, resilience factors, anxiety, depression, and interventions

Gluten-Free Diet Adherence Tools for Individuals with Celiac Disease: A Systematic Review and Meta-Analysis of Tools Compared to Laboratory Tests.

This systematic review aimed to find the tool that best predicts celiac individuals' adherence to a gluten-free diet (GFD). The Transparent Reporting of Multivariable Prediction Models for Individual Prognosis or Diagnosis (TRIPOD-SRMA) guideline was used for the construction and collection of data from eight scientific databases (PubMed, EMBASE, LILACS, Web of Science, LIVIVO, SCOPUS, Google Scholar, and Proquest) on 16 November 2023. The inclusion criteria were studies involving individuals with celiac disease (CD) who were over 18 years old and on a GFD for at least six months, using a questionnaire to predict adherence to a GFD, and comparing it with laboratory tests (serological tests, gluten immunogenic peptide-GIP, or biopsy). Review articles, book chapters, and studies without sufficient data were excluded. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) was used for data collection from the selected primary studies, and their risk of bias and quality was assessed using the Prediction Risk of Bias Assessment Tool (PROBAST). The association between the GFD adherence determined by the tool and laboratory test was assessed using the phi contingency coefficient. The studies included in this review used four different tools to evaluate GFD adherence: BIAGI score, Coeliac Dietary Adherence Test (CDAT), self-report questions, and interviews. The comparison method most often used was biopsy (n = 19; 59.3%), followed by serology (n = 14; 43.7%) and gluten immunogenic peptides (GIPs) (n = 4; 12.5%). There were no significant differences between the interview, self-report, and BIAGI tools used to evaluate GFD adherence. These tools were better associated with GFD adherence than the CDAT. Considering their cost, application time, and prediction capacity, the self-report and BIAGI were the preferred tools for evaluating GFD adherence.

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.

This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70-0.79 vs. 0.66-0.76, and for all sub-sites including oral cavity (0.73-0.89 vs. 0.69-0.77) and larynx (0.71-0.85 vs. 0.57-0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75-0.97, with an F1-score of 0.65-0.89 for HNC; AUROC of 0.61-0.91 and F1-score of 0.58-0.86 for the oral cavity; and AUROC of 0.76-0.97 and F1-score of 0.63-0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.

Safety of first-line systemic therapy in patients with metastatic colorectal cancer: a network meta-analysis of randomized controlled trials

ObjectiveTo evaluate the safety of first-line systemic therapy for metastatic colorectal cancer through network meta-analysis.MethodsThe literature from PubMed, Embase, Web of Science, and Cochrane Library databases was searched from the inception of the databases to August 15, 2023, and strict inclusion and exclusion criteria were applied to screen studies. The Cochrane Bias Risk Assessment Tool (RoB 2.0) was used to evaluate the quality of the included literature. Network meta-analysis was conducted using Stata 15.0 and R4.3.1 software to compare the incidence of adverse events (AEs) among different treatment regimens.ResultsA total of 53 randomized controlled trials, involving 17,351 patients with metastatic colorectal cancer (mCRC), were ultimately included, encompassing 29 different therapeutic approaches. According to SUCRA rankings, the CAPOX regimen is most likely to rank first in terms of safety, while the FOLFOXIRI + panitumumab regimen is most likely to rank last. In terms of specific AEs, the CAPOX regimen, whether used alone or in combination with targeted drugs (bevacizumab and cetuximab), is associated with a reduced risk of neutropenia and febrile neutropenia, as well as an increased risk of thrombocytopenia and diarrhea. The FOLFOX regimen, with or without bevacizumab, is linked to an increased risk of neutropenia and peripheral sensory neuropathy. The FOLFIRI/CAPIRI + bevacizumab regimen is associated with a reduced risk of peripheral sensory neuropathy. S-1 and S-1 + oxaliplatin are well-tolerated in terms of gastrointestinal reactions. The FOLFOXIRI regimen, whether used alone or in combination with targeted drugs, is associated with various AEs.ConclusionIn summary, the CAPOX regimen may be the safest option among the first-line systemic treatment regimens for mCRC patients, while the FOLFOXIRI + panitumumab regimen may be associated with a higher incidence of grade 3 or higher AEs.

Machine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review

ObjectiveThis study aimed to investigate the current status and performance of machine learning (ML) approaches in providing reproducible treatment response predictions. MethodsThis systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist. We searched PubMed, Cochrane Library, Web of Science, Scopus, and EBSCO databases for cohort studies that derived and/or validated ML models focused on predicting rheumatoid arthritis (RA) treatment response. We extracted data and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines. ResultsFrom 210 unduplicated records identified by the literature search, we retained 29 eligible studies. Of these studies, 10 developed a predictive model and reported a mean adherence to the TRIPOD guidelines of 45.6 % (95 % CI: 38.3–52.8 %). The remaining 19 studies not only developed a predictive model but also validated it externally, with a mean adherence of 42.9 % (95 % CI: 39.1–46.6 %). Most of the articles had an unclear risk of bias (41.4 %), followed by a high risk of bias, which was present in 37.9 %. ConclusionsIn recent years, ML methods have been increasingly used to predict treatment response in RA. Our critical appraisal revealed unclear and high risk of bias in most of the identified models, suggesting that researchers can do more to address the risk of bias and increase transparency, including the use of calibration measures and reporting methods for handling missing data. FundingNone.

Prevalence and progression rate of interstitial lung abnormalities detected on thoracic CT: a systematic review and meta-analysis.

To estimate the pooled prevalence and progression rate of ILAs and identify the risk factors for radiological progression. An EMBASE and PubMed search was undertaken, identifying all studies meeting the inclusion criteria performed before May 10, 2023. Random effect models were used to estimate pooled prevalence, ILA progression rates, and odds ratio for radiological progression based on radiological subtype. Subgroup analyses were performed to compare the general and high-risk populations for lung cancer. The quality of the included studies was evaluated using the risk of bias assessment tool for non-randomized studies. We analyzed 19 studies (241,541 patients) and 10 studies (1317 patients) for the pooled prevalence and progression rate of ILA, respectively. The pooled ILA prevalence was 9.7% (95% CI, 6.1-13.9%). The pooled prevalence was 6.8% (95% CI, 3.1-11.6%) and 7.1% (95% CI, 2.2-14.4%) in the general (six studies) and high-risk population for lung cancer (six studies), respectively. The pooled progression rate was 47.1% (95% CI, 29.1-65.5%). The pooled progression rate was 64.2% (95% CI, 45.0-81.2%, five studies) and 31.0% (95% CI, 8.2-60.5%, five studies) for longer (≥ 4.5 years) and shorter follow-up periods (< 4.5 years), respectively (p = 0.009). Fibrotic ILAs were significantly associated with a higher progression probability (combined OR, 5.55; 95% CI, 1.95-15.82). The prevalence of ILAs was approximately 9.7%. Approximately half of the patients exhibited radiological progression, with the rate increasing over a longer follow-up period. Fibrotic ILA was a significant risk factor for radiological progression. The prevalence of interstitial lung abnormalities (ILAs) is approximately 9.7%, with about half exhibiting progression; a longer follow-up duration and fibrotic ILAs are associated with a higher progression rate. ILAs are increasingly recognized as important, but few population data are available. ILAs exhibited a pooled prevalence of 9.7% with a progression rate of 47.1%. Fibrotic ILAs were associated with increased progression likelihood.

Effects of blood flow restriction training on physical fitness among athletes: a systematic review and meta-analysis

Blood flow restriction training (BFRT) is an effective, scientific and safe training method, but its effect on the overall quality of athletes remains unclear. The aim of this systematic review with meta-analysis was to clarify the effects of BFRT on the physical fitness among athletes. Based on the PRISMA guidelines, searches were performed in PubMed, Web of Science, SPORTDiscus, and SCOUPS, the Cochrane bias risk assessment tool was used to assess methodological quality, and RevMan 5.4 and STATA 15.0 software were used to analyze the data. A meta-analysis of 28 studies with a total sample size of 542 athletes aged 14–26 years and assessed as low risk for quality was performed. Our results revealed that the BFRT intervention had small to large improvements in the athletes' strength (ES = 0.74–1.03), power (ES = 0.46), speed (ES = 0.54), endurance (ES = 1.39–1.40), body composition (ES = 0.28–1.23), while there was no significant effect on body mass (p > 0.05). Subgroup analyses revealed that moderator variables (training duration, frequency, load, cuff pressure, and pressurization time) also had varying degrees of effect on athletes' physical fitness parameters. In conclusion, BFRT had a positive effect on the physical fitness parameters of the athletes, with significantly improved strength, power, speed, endurance and body composition, but not body mass parameters. When the training frequency ≥ 3 times/week, cuff pressure ≥ 160 mmHg, and pressurization time ≥ 10 min, the BFRT group was more favorable for the improvement of physical fitness parameters.

Performance of risk prediction models for diabetic foot ulcer: a meta-analysis.

The number of prediction models for diabetic foot ulcer (DFU) risk is increasing, but their methodological quality and clinical applicability are uncertain. We conducted a systematic review to assess their performance. We searched PubMed, Cochrane Library, and Embase databases up to 10 February 2024 and extracted relevant information from selected prediction models. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) checklist was used to assess bias risk and applicability. All statistical analyses were conducted in Stata 14.0. Initially, 13,562 studies were retrieved, leading to the inclusion of five development and five validation models from eight studies. DFU incidence ranged from 6% to 16.8%, with age and hemoglobin A1C (HbA1c) commonly used as predictive factors. All included studies had a high risk of bias, mainly due to disparities in population characteristics and methodology. In the meta-analysis, we observed area under the curve (AUC) values of 0.78 (95% CI [0.69-0.89]) for development models and 0.84 (95% CI [0.79-0.90]) for validation models. DFU risk prediction models show good overall accuracy, but there is a risk of bias. Adherence to the PROBAST checklist is crucial for improving their clinical applicability.

A systematic review on artificial intelligence evaluating PSMA PET scan for intraprostatic cancer.

To assess artificial intelligence (AI) ability to evaluate intraprostatic prostate cancer (PCa) on prostate-specific membrane antigen positron emission tomography (PSMA PET) scans prior to active treatment (radiotherapy or prostatectomy). This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: 'artificial intelligence', 'prostate cancer', and 'PSMA PET'. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used. Our search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high-grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) ≥3 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG ≥4 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non-PSMA-avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume. Although the current state of AI differentiating high-grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed.

Prevalence and Species Distribution of Neonatal Candidiasis: A Systematic Review and Meta-Analysis.

Candida infection is a significant cause of morbidity and mortality in neonatal intensive care units (NICU) globally. We aimed to conduct a systematic review to investigate the prevalence of candida among causative organisms of neonatal sepsis and identify the distribution of candida species infecting Saudi neonates. We comprehensively searched Web of Science, Scopus, PubMed, and Cochrane Library from their inception till November 2023. After screening titles, abstracts, and full texts, we ultimately included 21 eligible studies. The designs of the included studies were randomized clinical trials, cohorts, case-control, and case reports; the methodological quality was appraised using the Cochrane risk of bias assessment tool, NIH tool for observational studies, and Murad tool for assessing case reports. Our systematic review and meta-analysis pooled data reported in 21 studies in the Saudi populations, which provided data on different types of candidal infections in 2346 neonates. The pooled data of ten retrospective studies enrolling 1823 neonates revealed that candida species resembled 4.2% of the causative organisms of neonatal sepsis among Saudi neonates (95%CI [2.5%; 5.9%], p = 0.000). Additionally, out of a total of 402 candida species that were identified among the included studies, C. albicans prevailed mostly among Saudi neonates, followed by C. parapsilosis, NS candida, and C. tropicalis (50.25%, 21.40%, 12.44%, and 9.45%, respectively). We found that candida species prevailed in 4.2% of 1823 cases of neonatal sepsis; the most common candida species was C. albicans. We could not pool data regarding risk factors or susceptibility of candida species to different treatment modalities due to insufficient data, requiring future large-scale, high-quality studies to be conducted.

Combined traditional Chinese medicine therapy for the treatment of infertility with polycystic ovary syndrome: A network meta-analysis of randomized controlled trials.

Polycystic ovary syndrome (PCOS) infertility has attracted great attention from researchers due to its high incidence. Numerous studies have shown that Chinese medicine is effective in treating this disease, but there is a wide variety of Chinese medicine therapies available, and there is a lack of comparative evaluation of the efficacy of various Chinese medicine combination therapies in the clinic, which requires further in-depth exploration. This study aims to evaluate the efficacy of a combined traditional Chinese medicine (TCM) therapy for the treatment of infertility with PCOS using network meta-analysis (NMA). In PubMed, web of Science, Cochrane Library, Embase, China Knowledge Network, Wanfang Data, VIP Database, China Biomedical Literature Database (SinoMed) databases, searchs were conducted for information about the randomized controlled trials (RCTs) of combined TCM therapy for the treatment of infertility with PCOS. Quality evaluation was performed using the Cochrane 5.3 risk of bias assessment tool, and NMA using Stata 16.0. This study comprised 28 RCTs using 8 combined TCM therapies in total. The results of the NMA showed that moxibustion + herbal, fire acupuncture + herbal, acupuncture + herbal, electroacupuncture + herbal, and acupoint application + herbal improved the clinical pregnancy rate better than acupuncture, herbal, and western medicines monotherapy (P < .05). Additionally, ear point pressure + herbal enema + herbal, acupuncture and moxibustion + herbal, fire acupuncture + herbal, and acupuncture + herbal improved the ovulation rate better than acupuncture, herbal, and western medicines monotherapy (P < .05). Moxibustion + herbal, fire acupuncture + herbal, and acupuncture + herbal are the 3 most effective therapies for improving the clinical pregnancy rate. Fire acupuncture + herbal, acupuncture + herbal, and ear point pressure + herbal enema + herbal are the 3 most effective therapies for improving the ovulation rate. The combined TCM therapy demonstrated better efficacy for the treatment of infertility with PCOS compared to acupuncture, herbal, and western medicines monotherapy. However, the optimal treatment therapy varied depending on the outcome indicators. Further large sample, high-quality, and standardized RCTs are needed to verify these findings.

Prediction models for skin tears in the elderly: A systematic review and meta-analysis

BackgroundThe prevalence of risk prediction models for skin tears in the elderly is growing; however, there is still debate regarding the usefulness and suitability of these models for clinical use and additional study. ObjectiveThe purpose of this work is to perform a systematic review and meta-analysis of published research on skin tear risk prediction models in the elderly. MethodsWe conducted a comprehensive search of various databases, including Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, PubMed, Web of Science, MEDLINE, Scopus, The Cochrane Library, Wanfang Database, China Science and Technology Journal Database (VIP), and China National Knowledge Infrastructure (CNKI), from the beginning until November 27, 2023. Data extraction from the chosen studies encompassed various elements, such as study design, sample size, outcome definition, data source, predictors, model development, and performance. The assessment of bias and applicability was conducted using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was utilized to assess the transparency in reporting the prediction models—a meta-analysis of the most common predictors to assess predictor reliability. In addition, a narrative synthesis was carried out to provide an overview of the qualities, bias risk, and effectiveness of the current models. The reporting procedures of this meta-analysis conformed to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement. ResultsOut of the initially retrieved 1499 studies, this review included eight prediction models from eight selected studies. All the studies employed logistic regression to develop prediction models for skin tears. The prevalence of skin tears in the elderly varied from 3.0% to 33.3%. Senile purpura and a history of previous skin tears were the most commonly utilized predictors. The reported values for the area under the curve (AUC) ranged from 0.765 to 0.854. All the studies exhibited a high risk of bias, primarily due to inadequate reporting in the outcome and analysis domains. Furthermore, serious questions concerning their applicability were highlighted by four studies. ConclusionBased on the PROBAST checklist, the current models for predicting skin tears in the elderly showed a high risk of bias. The development of new prediction models with bigger sample sizes, appropriate study designs, and external validation from multiple sources ought to be the primary focus of future research. Patient or public contributionThere was no patient or public contribution to this systematic review. RegistrationPROSPERO registration number: CRD42023494387.