Ranked Probability Research Articles

The Clinical Efficacy and Safety of Anti-Viral Agents for Non-Hospitalized Patients with COVID-19: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials.

This network meta-analysis compared the clinical efficacy and safety of anti-viral agents for the prevention of disease progression among non-hospitalized patients with COVID-19. PubMed, Embase, Web of Science, Cochrane Library, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform were searched from their inception to 28 May 2022. Only randomized controlled trials (RCTs) that investigated the clinical efficacy of anti-viral agents for non-hospitalized patients with COVID-19 were included. Three RCTs involving 4241 patients were included. Overall, anti-viral agents were associated with a significantly lower risk of COVID-19 related hospitalization or death compared with the placebo (OR, 0.23; 95% CI: 0.06–0.96; p = 0.04). Compared with the placebo, patients receiving nirmatrelvir plus ritonavir had the lowest risk of hospitalization or death (OR, 0.12; 95% CI: 0.06–0.24), followed by remdesivir (OR, 0.13; 95% CI: 0.03–0.57) and then molnupiravir (OR, 0.67; 95% CI: 0.46–0.99). The rank probability for each treatment calculated using the P-score revealed that nirmatrelvir plus ritonavir was the best anti-viral treatment, followed by remdesivir and then molnupiravir. Finally, anti-viral agents were not associated with an increased risk of adverse events compared with the placebo. For non-hospitalized patients with COVID-19 who are at risk of disease progression, the currently recommended three anti-viral agents, nirmatrelvir plus ritonavir, molnupiravir and remdesivir, should continue to be recommended for the prevention of disease progression. Among them, oral nirmatrelvir plus ritonavir and intravenous remdesivir seem to be the better choice, followed by molnupiravir, as determined by this network meta-analysis. Additionally, these three anti-viral agents were shown to be as tolerable as the placebo in this clinical setting.

Neighborhood-Based Ensemble Evaluation Using the CRPS

Abstract The neighborhood-based ensemble evaluation using the continuous ranked probability score is based on the pooling of the cumulative density function (CDF) for all the points inside a neighborhood. This methodology can be applied to the forecast CDF for measuring the predictive input of neighboring points in the center of the neighborhood. It can also be applied at the same time to forecast CDF and observed CDF so as to quantify the quality of the pooled ensemble forecast at the scale of the neighborhood. Fair versions of these two neighborhood scores are also defined in order to reduce their dependencies on the size of ensemble forecasts. The borderline case of deterministic forecasts is also explored so as to be able to compare them with ensemble forecasts. The information of these new scores is analyzed on idealized and real cases of rain accumulated during 3 h and of 2-m temperature forecast by four deterministic and probabilistic forecasting systems operational at Météo-France.

Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool

Recently, the conceptual data-driven approach (CDDA) was proposed to correct residuals of ensemble hydrological models (HMs) using data-driven models (DDMs), followed by the stochastic CDDA (SCDDA) that used HM simulations as input to DDMs within a stochastic framework - both approaches improved ensemble HMs' simulations. Here, a new SCDDA is introduced where CDDA uncertainty is estimated (instead of DDM uncertainty in the original SCDDA). Using nine HM-DDM combinations for daily streamflow simulation in three Swiss catchments, the new SCDDA improved CDDA's mean continuous ranked probability score up to 15% and performed similarly without a snow-routine in a snowy catchment, suggesting that SCDDA may account for missing processes in HMs. The stochastic framework can convert unreliable ensemble models into more reliable (stochastic) models at the cost of simulation sharpness. The coverage probability plot is proposed as a diagnostic tool, predicting SCDDA's out-of-sample reliability using validation set data (CDDA simulations and observations).

Efficacy of Transarterial Chemoembolization Combined with Molecular Targeted Agents for Unresectable Hepatocellular Carcinoma: A Network Meta-Analysis.

Simple SummaryLiver cancer is the second most common cause of cancer-related death, with hepatocellular carcinoma (HCC) being the most prevalent subtype. Transarterial chemoembolization (TACE) in combination with different tyrosine kinase inhibitors (TKIs) has recently been widely used for unresectable HCC (uHCC). However, studies investigating different combinations of agents have shown inconsistent results. Thus, we conducted a network meta-analysis to assess and compare the response of different agents in an uHCC setting. According to our results, TACE plus lenvatinib provides optimal treatment for uHCC, with the highest ranking based on OS, PFS, and DCR rates and the second-best ranking based on ORR rates.Transarterial chemoembolization (TACE) combined with tyrosine kinase inhibitors (TKIs) is the mainstay treatment for unresectable hepatocellular carcinoma (uHCC). However, studies investigating different combinations of agents have shown inconsistent results. Here, we used network meta-analysis (NMA) to compare different agents across 41 studies (36 cohort studies and five RCTs) in 11,540 patients. Multiple RCTs and cohort studies were searched to evaluate TACE combined with different TKIs. Outcomes of interest included overall survival (OS), progression-free survival (PFS), and tumor response. NMA used a random-effects consistency model to pool evidence from direct and indirect comparisons. Hazard ratio (HR) and relative risks (RR) with 95% confidence intervals (CI) were analyzed. Further, heterogeneity and publication bias analyses were performed and agents were ranked. TACE plus lenvatinib provided the maximal OS (Rank probability: 0.7559), PFS (Rank probability: 0.8595), CR (Rank probability: 0.4179), and DCR (Rank probability: 0.3857). TACE plus anlotinib demonstrated the highest PR (p = 0.62649) and ORR (p = 0.51158). SD was more often associated with TACE plus sorafenib (Rank probability: 0.601685). TACE plus lenvatinib provides optimal treatment for uHCC based on the highest ranking of OS, PFS, and DCR rates. However, given the lack of statistically significant OS benefit, shared decision making should include other TKIs as acceptable alternatives.

Device-Assisted and Neuromodulatory Therapies for Parkinson's Disease: A Network Meta-Analysis.

Device-assisted and neuromodulatory therapies are the standard of care for Parkinson's disease (PD) with disabling motor complications. We aimed to compare and rank the currently available advanced therapies for PD on patient relevant outcomes. We searched various databases for randomized controlled trials that studied subthalamic nucleus deep brain stimulation (STN-DBS), globus pallidus interna (GPi) DBS, pallidotomy, subthalamotomy, continuous subcutaneous apomorphine infusion (CSAI), or intrajejunal levodopa infusion (IJLI), in patients with PD and motor complications. Primary outcome was the quality of life (QOL) at 6 months. Secondary outcomes included Unified Parkinson's Disease Rating Scale III and II, ON time, OFF time, levodopa equivalent daily doses, and adverse events (AE). Data were pooled using a Bayesian network meta-analysis, summarized as mean difference (MD) with 95% credibility intervals (CrI) and visualized in forest plots/league tables. Surface under the cumulative ranking curve plots determined the ranking probability. We identified 6745 citations and included 26 trials. STN-DBS (MD, -8.0; 95% CrI, -11, -5.8), GPi-DBS (MD, -7.1; 95% CrI, -11, -2.9), and IJLI (MD, -7.0; 95% CrI, -12, -1.8) led to better QOL than medical therapy alone, without significant differences among them. STN-DBS had the highest probability of being ranked the best treatment for QOL (79.6%), followed by IJLI (63.5%) and GPi-DBS (62.8%). In advanced PD, STN-DBS alleviates more patient and clinician relevant outcomes, followed by GPi-DBS and IJLI. In resource limited settings, unilateral pallidotomy may improve motor symptoms and activities of daily living, although overall QOL may not be improved. © 2022 International Parkinson and Movement Disorder Society.

Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation

Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation (LUBE) using neural networks with two outputs to make probabilistic modeling for predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), continuous ranked probability score (CRPS), and loss, which is the integration of PICP and MPIW, by removing unnecessary features through feature selection. When features with high gain were selected by random forest (RF), in the modeling of 14.7 kW PV systems, loss improved by 1.57 kW, CRPS by 0.03 kW, PICP by 0.057 kW, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise and reduce the modeling accuracy.

Forward variable selection for random forest models

Random forest is a popular prediction approach for handling high dimensional covariates. However, it often becomes infeasible to interpret the obtained high dimensional and non-parametric model. Aiming for an interpretable predictive model, we develop a forward variable selection method using the continuous ranked probability score (CRPS) as the loss function. eOur stepwise procedure selects at each step a variable that minimizes the CRPS risk and a stopping criterion for selection is designed based on an estimation of the CRPS risk difference of two consecutive steps. We provide mathematical motivation for our method by proving that in a population sense, the method attains the optimal set. In a simulation study, we compare the performance of our method with an existing variable selection method, for different sample sizes and correlation strength of covariates. Our method is observed to have a much lower false positive rate. We also demonstrate an application of our method to statistical post-processing of daily maximum temperature forecasts in the Netherlands. Our method selects about 10% covariates while retaining the same predictive power.

Comparative efficacy and safety of fimasartan in patients with hypertension: A network meta-analysis of randomized controlled trials.

Hypertension is a prevalent risk factor for cardiovascular disease. Angiotensin II receptor blockers are widely prescribed to patients with hypertension, while new drugs are continuously developed. However, data on comparative efficacy and safety of novel agents, such as fimasartan, are scarce. Here, we aimed to collect clinical evidence on different angiotensin II receptor blockers using a network meta‐analysis. Randomized controlled trials whose follow‐up time is within 12 weeks were identified from eight databases via a systematic literature review. Of the 7909 possibly relevant studies, 61 studies with 14,249 adult patients were included in the analysis. These studies were further subjected to quality appraisal using Cochran's Risk of Bias, and sitting systolic blood pressure was considered the primary endpoint. A Bayesian random effect generalized linear model was used for the network meta‐analysis, and the treatment rank probability was determined. Olmesartan (standardized mean difference ‐0.987 [‐1.29, ‐0.729]) and fimasartan (standardized mean difference ‐0.966 [‐1.21, ‐0.745]) showed the highest rank probabilities (37% and 35%) in the 4‐week group, considering the primary endpoint. Furthermore, the odds ratio of adverse events for all agents did not differ significantly from that of the placebo. The treatment rank of angiotensin II receptor blockers varied depending on the outcome type and follow‐up period considerably. Fimasartan rapidly lowered blood pressure in 4 weeks, which was further maintained until 12 weeks, indicating its competent efficacy and tolerability. Our findings may help medical practitioners and patients to select the best angiotensin II receptor blocker against hypertension.

Comparative Efficacy and Safety of Janus Kinase Inhibitors and Secukinumab in Patients with Active Ankylosing Spondylitis: A Systematic Review and Meta-Analysis

Background: Janus kinase (JAK) inhibitors and secukinumab have been demonstrated to be effective treatments for ankylosing spondylitis (AS). However, there have been no head-to-head trials comparing the effectiveness and safety characteristics of JAK inhibitors with secukinumab. This study aimed to evaluate the relative effectiveness and safety of JAK inhibitors and secukinumab in patients with active AS. Summary: A Bayesian network meta-analysis was conducted using direct and indirect data from randomized controlled trials (RCTs) that examined the efficacy and safety of tofacitinib 5 mg, upadacitinib 15 mg, filgotinib 200 mg, and secukinumab 150 mg in patients with active AS who had a poor response or intolerance to nonsteroidal anti-inflammatory drugs (NSAIDs) and were tumor necrosis factor (TNF) inhibitor-naïve. Data from six RCTs comprising 937 patients were analyzed. The Assessment of SpondyloArthritis International Society 20 (ASAS20) response rates were significantly higher in the JAK inhibitors and secukinumab groups than in the placebo group. The surface under the cumulative ranking curve (SUCRA)-based ranking probability based on the ASAS20 response rate suggested that tofacitinib 5 mg had the highest likelihood of being the best treatment for achieving the ASAS20 response rate, followed by filgotinib 200 mg, upadacitinib 15 mg, secukinumab 150 mg, and placebo. The SUCRA-based ranking probability based on the ASAS20 response rate suggested that tofacitinib 5 mg had the highest likelihood of being the best treatment for achieving the ASAS40 response rate, followed by upadacitinib 15 mg, secukinumab 150 mg, filgotinib 200 mg, and placebo. Key Messages: Tofacitinib 5 mg was the most effective treatment for AS, whereas JAK inhibitors and secukinumab 150 mg were effective treatments in patients with active AS who had a poor response or intolerance to NSAIDs and were TNF inhibitor-naïve.

Poster 186: Repair of the Distal Biceps Tendon: A Systematic Review & Network Meta-Analysis of Biomechanical Performance

Objectives:Rupture of the distal biceps is common and demonstrates excellent short- and long-term clinical outcomes following surgical repair. Prior studies have individually evaluated the biomechanical construct strength of multiple fixation devices, but none has comprehensively evaluated constructs in a head-to-head comparison. We sought to compare the failure strength, maximum strength, stiffness and displacement of available constructs for distal biceps repair.Methods:A network meta-analysis was conducted to determine the relative performance of 2 all-suture suture anchors, 2 intramedullary cortical buttons, 2 suture anchors, extramedullary cortical buttons, extramedullary cortical button plus interference screw, interference screw, single intramedullary cortical button, single suture anchor, trans-osseous suture, tension slide technique and tension slide technique plus suture tape. Analysis consisted of arm-based network meta-analysis under Bayesian random-effects model with Markov Chain Monte Carlo (MCMC) sampling. Geometry of the network was represented visually via a network plot with nodes representing treatments and edges representing direct comparative evidence. Biomechanical outcomes of fixation techniques were summarized as treatment effects and their corresponding 95% confidence intervals (CI). Rank probabilities were calculated and used to generate each treatment’s surface under the cumulative ranking (SUCRA) curve.Results:A total of 21 studies were included in the network meta-analysis. For failure strength, the two intramedullary cortical buttons ranked highest (92.73%). For maximum strength, the extramedullary cortical button alone (86.96%) and extramedullary button plus interference screw (83.40%) ranked highest. The two intramedullary cortical buttons ranked highest in stiffness (98.75%), followed by the extramedullary cortical button with interference screw (88.75%) and extramedullary cortical button (75.00%). Finally, with respect to demonstrating the least displacement after loading, the two suture anchors (89.43%) ranked highest, followed by tension slide technique with suture tape (73.14%) and extramedullary cortical button (72.71%).Conclusions:The fixation constructs that demonstrated the greatest overall biomechanical performance in distal biceps tendon repair were the extramedullary cortical button and two intramedullary cortical buttons. Two suture anchors demonstrated the least displacement after loading.Figure 1.Cochrane risk of bias summary of included studies.Figure 2.Evidence networks for outcomes of failure strength (A), maximum strength (B), stiffness (C), and displacement (D). The thickness of each line connecting two nodes is proportional to how often these two interventions were compared, while the size of each node is proportional to the respective patient sample size.Figure 3.Forest plot displaying the effect difference of each intervention on (A) failure strength, (B) maximum strength, (c) stiffness, and (D) displacement.

Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems

Probabilistic load forecasting (PLF) aims to predict the future uncertainties of loads to reduce the potential risks in power system planning and operation. In the increasingly complex power market environment, exploring advanced approaches to obtain more accurate PLF is still a significant topic. Optimizing individual forecasting method is no longer the only direction to improve the accuracy of load forecasting in recent years. Researchers started to focus on combination methods because of their better accuracy in most cases than a single model. There are existing combination methods designed for parametric environment, where some results are based on the certain assumption (e.g., Gaussian distribution assumption of single prediction). Combining probabilistic forecasts in nonparametric environment is rarely investigated, because modeling the combination problem without assuming distributions of parameters is hard. This paper proposes a novel combined model for probabilistic forecasting tailored to nonparametric environments, which combines multiple quantile-based models by minimizing the overall loss function composed of continuous ranked probability score (CRPS) under kernel density estimation (KDE). We define a multilayer Gaussian mixture distribution, which is an extended form of Gaussian mixture distribution that can simulate any distribution type in nonparametric environment. Based on the multilayer Gaussian mixture distribution, the combined model is further formulated into a quadratic programming problem with linear restrictions that can be solved efficiently. Case studies are performed using benchmark and competition datasets from the United States and China. The results show that our proposed method outperforms the best individual model and other existing combination methods. In summary, this paper constructs a complete theoretical framework of nonparametric probabilistic combination forecasting and proves its effectiveness in practical application.

How Skillful Are the European Subseasonal Predictions of Wind Speed and Surface Temperature?

Abstract Subseasonal forecasts of 100-m wind speed and surface temperature, if skillful, can be beneficial to the energy sector as they can be used to plan asset availability and maintenance, assess risks of extreme events, and optimally trade power on the markets. In this study, we evaluate the skill of the European Centre for Medium-Range Weather Forecasts’ subseasonal predictions of 100-m wind speed and 2-m temperature. To the authors’ knowledge, this assessment is the first for the 100-m wind speed, which is an essential variable of practical importance to the energy sector. The assessment is carried out on both forecasts and reforecasts over European domain gridpoint wise and also by considering several spatially averaged domains, using several metrics to assess different attributes of forecast quality. We propose a novel way of synthesizing the continuous ranked probability skill score. The results show that the skill of the forecasts and reforecasts depends on the choice of the climate variable, the period of the year, and the geographical domain. Indeed, the predictions of temperature are better than those of wind speed, with enhanced skill found for both variables in the winter relative to other seasons. The results also indicate significant differences between the skill of forecasts and reforecasts, arising mainly due to the differing ensemble sizes. Overall, depending on the choice of the geographical domain and the forecast attribute, the results show skillful predictions beyond 2 weeks, and in certain cases, up to 6 weeks for both variables, thereby encouraging their implementation in operational decision-making.

Network Meta-Analysis of Open Reduction Internal Fixation, Intramedullary Nail Fixation, and Distal Femoral Replacement for Periprosthetic Distal Femur Fractures

Background: Although open reduction internal fixation (ORIF) with periarticular locking plates, retrograde intramedullary nail (IMN) fixation, and distal femoral replacement (DFR) have been widely used for the treatment of periprosthetic distal femur fractures (PPDFF), it remains unclear which one may be an optimal surgical strategy.Objective: This network meta-analysis aimed to determine the optimal surgical strategy for PPDFF from ORIF, IMN, and DFR by evaluating major complication rates and revision rates.Patients and Methods: We identified eligible studies by performing an updated literature search in PubMed, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) and checking published meta-analyses. The methodological index for non-randomized studies (MINORS) was used to assess the methodological quality of eligible studies. Major complications and revision rates were defined as outcomes of interest. All statistical analyses were performed by ADDIS 1.4 software.Results: We included 17 eligible studies in the final analysis. Statistical analyses did not reveal significant differences when ORIF was compared with IMN and ORIF compared with DFR, and IMN compared with DFR in terms of two clinical outcomes. However, DFR achieved a beneficial trend in major complications [odds ratio (OR) = 1.34; 95% CrI = 0.48–3.80] and revision (OR = 1.64; 95% CrI = 0.68–4.38) rates compared with IMN. Ranking probabilities indicate that DFR has the highest probability of being the optimal option, followed by ORIF and IMN.Conclusion: There was no difference in major complications or reoperations between the three treatments. However, ORIF with periarticular locking plates may be a preferred alternative option because DFR is limited by the high cost. Moreover, our study highlights the need for future prospective trials evaluating the outcomes of these three surgical strategies.

Determining the Optimal (Neo)Adjuvant Regimen for Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer Regarding Survival Outcome: A Network Meta-Analysis.

BackgroundThe optimal (neo)adjuvant regimen for human epidermal growth factor receptor-2 (HER2)-positive breast cancer regarding survival outcomes remains unclear.MethodsWe searched Web of Science, PubMed, and the Cochrane Central Register of Controlled Trials systematically to find out randomized controlled studies, up to January 2022, that compared different anti-HER2 regimens in the (neo)adjuvant setting. The primary endpoint was disease-free survival (DFS). We used a Bayesian statistical model to combine direct and indirect comparisons and used odds ratios (ORs) to pool effect sizes and performed the surface under the cumulative ranking area (SUCRA) curves to estimate the ranking probabilities of various regimens. For survival outcomes, we performed two parallel analyses, one based on data from both neoadjuvant and adjuvant studies and the other specific to adjuvant studies. All statistics were two-sided.ResultsFifteen studies were finally enrolled. Regarding DFS, the overall analysis indicated that the top two regimens for HER2-positive breast cancer were chemotherapy plus trastuzumab with lapatinib, and chemotherapy plus trastuzumab with pertuzumab (SUCAR of 81% and 79%, respectively), with the OR of 0.99 [95% confidence interval (CI), 0.59 to 1.54]; the parallel analysis specific to adjuvant trials indicated that the top two regimens were chemotherapy plus trastuzumab with sequential neratinib, and chemotherapy plus trastuzumab with pertuzumab (SUCRA of 80% and 76%, respectively), with the OR of 1.04 (95% CI, 0.63 to 1.73). The dual-target therapy that combines trastuzumab and pertuzumab showed the highest risk of inducing cardiac events, with an SUCRA of 92%.ConclusionsChemotherapy plus trastuzumab and pertuzumab might be the optimal regimen for HER2-positive breast cancer in improving the survival rate. However, the cardiotoxicity of this dual-target therapy should be taken care of.

Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation

Abstract. In this study, we assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities in western Europe. The SWG is based on a random sampling of analogs of the geopotential height at 500 hPa (Z500). The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) compared with persistence and climatology forecasts for lead times of 5 and 10 d for different areas in Europe. We find that the low predictability episodes of our model are related to specific weather regimes, depending on the European region. Comparing the SWG forecasts to ECMWF forecasts, we find that the SWG shows a good performance for 5 d. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g., region for analogs) must be tuned for each region in order to optimize its performance.

Acupuncture and Moxibustion in the Treatment of Adult Diarrhea Irritable Bowel Syndrome: A Network Meta-analysis.

Objective This study was aimed at comparing the clinical efficacy of acupuncture and moxibustion on irritable bowel syndrome complicated with diarrhea (IBS-D) in adults and providing guidance for clinical treatment. Methods PubMed, The Cochrane Library, Embase, CBM, CNKI, and VIP and Wanfang databases were searched to obtain clinical randomized controlled trials (RCTs) on acupuncture and moxibustion in the treatment of IBS-D published from establishment of the database to August 5, 2021. Relevant data were extracted to assess the risk of bias in the included studies, and statistical software Stata 16.0 was used for meta-analysis. Results Twenty-one studies were eventually included in the network meta-analysis (NMA), including 1626 patients with IBS-D and 8 therapeutic measures. NMA showed that acupuncture [OR = 0.35, 95%CI (0.25, 0.49), P < 0.05], warming needle moxibustion [OR = 6.34, 95%CI (2.83, 14.21), P < 0.05], acupuncture+sandwiched moxibustion [OR = 12.83, 95%CI (4.49, 36.64), P < 0.05], acupuncture+heat-sensitive moxibustion [OR = 9.86, 95%CI (1.77, 55.00), P < 0.05] were more effective than pinaverium bromide in the treatment of IBS-D. Cumulative ranking probability (SUCRA) showed that the comprehensive efficacy of acupuncture and moxibustion (86.8%) and quality of life (QOL) (70.4%) was the best, while the comprehensive efficacy of pinaverium bromide (2.1%) and QOL (16.3%) was the worst. GV20, GV29, ST 25, ST37, ST36, SP6, LR3, and CV12 were used frequently. Conclusion Acupuncture+sandwiched moxibustion has the best effect on improving the efficacy and QOL of IBS-D patients. Limited by the number and quality of studies, we still need a large sample, multicenter, and high-quality clinical trials to confirm our findings.

Automatic binary and ternary change detection in SAR images based on evolutionary multiobjective optimization

In most of the previous works, changed and unchanged regions are detected by analyzing the changes of backscattering coefficients for SAR images, which is termed as binary change detection. In fact, due to the increase and decrease of backscattering coefficients, the changed regions can be further analyzed as two kinds of changes, which is termed as ternary change detection. In this paper, a change detection method based on evolutionary multiobjective optimization is proposed to automatically perform binary and ternary change detection of multitemporal SAR images. First, the log-likelihood function of the Gaussian mixture model and the Bhattacharyya distance are designed as two objectives, respectively. In particular, a novel measurement method based on Bhattacharyya distance is designed for the ternary change detection task. Not only the separability between each two classes is maximized, but also the Bhattacharyya distance between two changed classes and unchanged class is kept closer to obtain a more balanced classification performance. Then a multiobjective optimization method based on non-dominated sorting is used to optimize these two objectives simultaneously. In the proposed approach, chromosome ranking and perturbation probability selection operators are designed to make high-quality solutions with a high probability of being exploited and improve the performance of the algorithm. In addition, a one-step local search strategy based on the expectation–maximization method is integrated into the proposed algorithm to accelerate the convergence. Experimental results on simulated and real-world datasets demonstrate the effectiveness and robustness of the proposed algorithm.

Effect of Eight Different Methods for Preventing Incontinence-associated Dermatitis: A Network Meta-analysis

The objective is to systematically evaluate the effects of different methods in the prevention of incontinence-associated dermatitis. Methods is based on Searched PubMed, the Cochrane Library, Web of Science, EMbase, CNKI, VIP and CBM,and the search time limit was all built until January 2019.Two investigators independently screened the literature, extracted the data and evaluated the risk of bias in the included studies. The R 3.5.2 software gemtc package, JAGS 4.3.0, and Stata 13.0 software were used for analysis. Results: A total of 24 articles were included and 2367 patients were included. Mesh meta-analysis results show that the skin protective agent, ostomy bag combined drainage device, ostomy bag, ostomy bag combined with skin protectant, built-in tampon to prevent the incidence of incontinence-related dermatitis is better than conventional care; The effect of devices and ostomy bags on preventing the incidence of incontinence-associated dermatitis is better than that of drainage devices and built-in tampon; the effect of ostomy pouch combined with skin protectant on preventing the incidence of incontinence-associated dermatitis is better than that of built-in tampon. The ranking probability map shows that the ostomy combined drainage device is a better solution to prevent the incidence of incontinence-associated dermatitis. Conclusion: According to the results of mesh meta-analysis and probabilistic ranking, the effect of the laparoscopic combined drainage device bag on preventing the incidence of incontinence-associated dermatitis is better than other methods. Future research should also be focused on the direct comparison of effects between different methods and the evaluation of cost effectiveness.

Surgical prevention of pharyngocutaneous fistula in salvage total laryngectomy: a systematic review and network meta-analysis.

To compare the efficacy of different reconstructive techniques in preventing pharyngocutaneous fistula (PCF) after salvage total laryngectomy (STL). An arm-based network analysis was conducted using a Bayesian hierarchical model according to the PRISMA-NMA guidelines. A total of 1694 patients with a median age of 64 years (n = 1569, 95% CI: 62-66 years) were included. If compared to primary pharyngeal closure alone, only a pedicled flap onlay (PFO) showed a statistically significant reduction in PCF rate (OR: 0.35, CI: 0.20-0.61). PFO seemed to perform better than other treatments according to the rank probabilities test (39.9% chance of ranking first). A pedicled flap placed with an overlay technique might be preferred over a patch reconstruction to prevent PCF after STL.

Machine learning-based gene alteration prediction model for primary lung cancer using cytologic images.

Understanding the gene alteration status of primary lung cancers is important for determining treatment strategies, but gene testing is both time-consuming and costly, limiting its application in clinical practice. Here, potential therapeutic targets were selected by predicting gene alterations in cytologic specimens before conventional gene testing. This was a retrospective study to develop a cytologic image-based gene alteration prediction model for primary lung cancer. Photomicroscopic images of cytology samples were collected and image patches were generated for analyses. Cancer-positive (n=106) and cancer-negative (n=32) samples were used to develop a neural network model for selecting cancer-positive images. Cancer-positive cases were randomly assigned to training (n=77) and validation (n=26) data sets. Another neural network model was developed to classify cancer images of the training data set into 4 groups: anaplastic lymphoma kinase (ALK)-fusion, epidermal growth factor receptor (EGFR), or Kirsten rat sarcoma viral oncogene homologue (KRAS) mutated groups, and other (None group), and images of the validation data set were classified. A decision algorithm to predict gene alteration for cases with 3 probability ranks was developed. The accuracy and precision for selecting cancer-positive patches were 0.945 and 0.991, respectively. Predictive accuracy for the EGFR and KRAS groups in the validation data set was ~0.95, whereas that for the ALK and None groups was ~0.75 and ~ 0.80, respectively. Gene status was correctly predicted in the probability rank A cases. The model extracted characteristic conventional cytologic findings in images and a novel specific feature was discovered for the EGFR group. A gene alteration prediction model for lung cancers by machine learning based on cytologic images was successfully developed.