Estimates Of Effects Research Articles

Exploring heterogeneity of treatment effect in patients with sepsis: Protocol for a scoping review.

The average treatment effect (ATE) reported by most randomised clinical trials provides estimates of treatment effects for the theoretical, non-existent average patient. However, ATE may not accurately reflect the outcomes for all subsets of the trial population; some individuals may benefit from the intervention, while others experience worse outcomes or no effect at all. Heterogeneity of treatment effect (HTE) is the non-random and explainable variation in the magnitude or direction of a treatment effect among individuals within a population. Predictive approaches to HTE seek to provide estimates of which treatment of choice is better suited for the individual patient, using regression and/or machine learning techniques. This scoping review aims to investigate the extent to which such predictive approaches to HTE are applied to data from trials on sepsis or septic shock as well as the results of these analyses. The planned review will be conducted in accordance with the PRISMA extension for scoping reviews. We will search Medline, EMBASE, Central, Cinahl and Google Scholar for studies on sepsis or septic shock in which HTE was analysed using predictive approaches. We plan to chart data regarding trial characteristics, patient demographics, disease severity, interventions, outcomes of interest and ATEs, type of predictive approach for the HTE analysis, results from HTE analysis and whether HTE analysis would change an ATE-based trial conclusion. Studies included in the scoping review will be presented as narrative summaries, supplemented with descriptive statistics of quantitative data. The planned scoping review will systematically investigate, summarise and delineate the existing evidence of analysis of HTE in trials on sepsis or septic shock patients as well as their findings, when performed using predictive approaches.

On the estimation of quantile treatment effects using a semiparametric propensity score

. This article considers the estimation of quantile treatment effects under the assumption of unconfoundedness given quasi-experimental data. We propose a semiparametric single-index method to estimate the propensity score. Our approach overcomes the curse of dimensionality issue of a nonparametric propensity score and can handle a moderately large dimension of covariates. It is more flexible than the parametric propensity score and thereby alleviates the possible model misspecification problem. We derive the asymptotic distribution of the quantile treatment effect estimator that is based on the semiparametric propensity score. We also propose a consistent variance estimator and construct the confidence intervals for the QTE estimator. Monte Carlo simulation results show that the proposed estimator performs well in finite samples and the confidence intervals have adequate coverage rates. We demonstrate the usefulness of our method by applying it to a study of the quantile treatment effects of college education on income.

Termination of Resuscitation Rules and Survival Among Patients With Out-of-Hospital Cardiac Arrest

Termination of resuscitation (TOR) rules may help guide prehospital decisions to stop resuscitation, with potential effects on patient outcomes and health resource use. Rules with high sensitivity risk increasing inappropriate transport of nonsurvivors, while rules without excellent specificity risk missed survivors. Further examination of the performance of TOR rules in estimating survival of out-of-hospital cardiac arrest (OHCA) is needed. To determine whether TOR rules can accurately identify patients who will not survive an OHCA. For this systematic review and meta-analysis, the MEDLINE, Embase, CINAHL, Cochrane Library, and Web of Science databases were searched from database inception up to January 11, 2024. There were no restrictions on language, publication date, or time frame of the study. Two reviewers independently screened records, first by title and abstract and then by full text. Randomized clinical trials, case-control studies, cohort studies, cross-sectional studies, retrospective analyses, and modeling studies were included. Systematic reviews and meta-analyses were reviewed to identify primary studies. Studies predicting outcomes other than death, in-hospital studies, animal studies, and non-peer-reviewed studies were excluded. Data were extracted by one reviewer and checked by a second. Two reviewers assessed risk of bias using the Revised Quality Assessment Tool for Diagnostic Accuracy Studies. Cochrane Screening and Diagnostic Tests Methods Group recommendations were followed when conducting a bivariate random-effects meta-analysis. This review followed the Preferred Reporting Items for a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement and is registered with the International Prospective Register of Systematic Reviews (CRD42019131010). Sensitivity and specificity tables with 95% CIs and bivariate summary receiver operating characteristic (SROC) curves were produced. Estimates of effects at different prevalence levels were calculated. These estimates were used to evaluate the practical implications of TOR rule use at different prevalence levels. This review included 43 nonrandomized studies published between 1993 and 2023, addressing 29 TOR rules and involving 1 125 587 cases. Fifteen studies reported the derivation of 20 TOR rules. Thirty-three studies reported external data validations of 17 TOR rules. Seven TOR rules had data to facilitate meta-analysis. One clinical study was identified. The universal termination of resuscitation rule had the best performance, with pooled sensitivity of 0.62 (95% CI, 0.54-0.71), pooled specificity of 0.88 (95% CI, 0.82-0.94), and a diagnostic odds ratio of 20.45 (95% CI, 13.15-31.83). In this review, there was insufficient robust evidence to support widespread implementation of TOR rules in clinical practice. These findings suggest that adoption of TOR rules may lead to missed survivors and increased resource utilization.

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment.

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid-base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

The Effect of Fenugreek on the Severity of Dysmenorrhea: A Systematic Review and Meta-analysis.

Dysmenorrhea is the most common periodic pain, which affects more than 50% of women with regular menstruation. Fenugreek is one of the medicinal plants with analgesic properties. This study aimed to determine the effect of fenugreek application in the severity of dysmenorrhea and its side effects in women with dysmenorrhea. PICO: Population: women with dysmenorrhea; Intervention: fenugreek; Comparison: control groups; and Outcome: reduction in the severity of dysmenorrhea and its side effects. English database (PubMed, Cochrane Library, Scopus, and Web of Science) and Persian database [SID (Scientific Information Database) and Magiran] were used for research until February 11, 2023, using the keywords "Dysmenorrhea [Mesh]," "Foenum [Mesh]," "fenugreek [Mesh]," and "Trigonella [Mesh]." The reference list of the selected articles was also checked. The quality assessment was conducted through the Cochrane Handbook for Systematic Reviews of Interventions version 5.2.0. The RevMan 5.3 software was used to analyze and report the data of the entered studies. Meta-analysis results were reported with the standardized mean difference (95% confidence interval). A subgroup analysis was performed based on the type of control groups. The quality of evidence was assessed using the GRADE approach. After removing duplicates and ineligible cases, four articles were included in the systematic review out of the 1526 records obtained. The results showed that the pain intensity caused by primary dysmenorrhea decreased with fenugreek compared to placebo (pooled result SMD: -2.21; 95% CI: -3.26 to -1.17; Z: 4.17; p <0.001). There was no significant difference between fenugreek with mefenamic acid (SMD: 0.05; 95% CI: -0.57 to 0.67; Z: 0.17; p = 0.86) and fenugreek with Chandrasura churna (SMD: 0.06; 95% CI: -0.56 to 0.68; Z: 0.19; p = 0.85). Bias, in terms of incomplete outcome data and selective reporting, was low risk in all studies, and the available evidence was low quality according to the GRADE approach. The results showed that the effect of fenugreek on pain intensity in dysmenorrhea is highly uncertain. The true effect is likely to be substantially different from the estimate of effect. Regarding the importance of the health and quality of life of women of reproductive age and the low quality of evidence of the studies, clinical trials with stronger methodology are suggested in this field.

Evaluación invasiva de la disfunción microvascular coronaria y resultados cardiovasculares en todo el espectro de la EC: un metanálisis

Introduction and objectivesCoronary microvascular dysfunction (CMD) is highly prevalent and is recognized as an important clinical entity in patients with coronary heart disease (CHD). Nevertheless, the association of CMD with adverse cardiovascular events in the spectrum of CHD has not been systemically quantified. MethodsWe searched electronic databases for studies on patients with CHD in whom coronary microvascular function was measured invasively, and clinical events were recorded. The primary endpoint was major adverse cardiac events (MACE), and the secondary endpoint was all-cause death. Estimates of effect were calculated using a random-effects model from published risk ratios. ResultsWe included 27 studies with 11 404 patients. Patients with CMD assessed by invasive methods had a higher risk of MACE (RR, 2.18; 95%CI, 1.80-2.64; P<.01) and all-cause death (RR, 1.88; 95%CI, 1.55-2.27; P<.01) than those without CMD. There was no significant difference in the impact of CMD on MACE (interaction P value=.95) among different invasive measurement modalities. The magnitude of risk of CMD assessed by invasive measurements for MACE was greater in acute coronary syndrome patients (RR, 2.84, 95%CI, 2.26-3.57; P<.01) than in chronic coronary syndrome patients (RR, 1.77, 95%CI, 1.44-2.18; P<.01) (interaction P value<.01). ConclusionsCMD based on invasive measurements was associated with a high incidence of MACE and all-cause death in patients with CHD. The magnitude of risk for cardiovascular events in CMD as assessed by invasive measurements was similar among different methods but varied among CHD populations.

Estimates of the Isotope Effect for Nitrate Assimilation in the Indian Sector of the Southern Ocean

AbstractThe Southern Ocean is a high‐nutrient, low‐chlorophyll (HNLC) region characterized by incomplete nitrate (NO3−) consumption by phytoplankton in surface waters. During this incomplete consumption, phytoplankton preferentially assimilate the 14N‐ versus the 15N‐bearing form of NO3−, quantified as the NO3− assimilation isotope effect (15ε). Previous summertime estimates of the 15ε from HNLC regions range from 4 to 11‰. While culture work has shown that the 15ε varies among phytoplankton species, as well as with light and iron stress, we lack a systematic understanding of how and why the 15ε varies in the field. Here we estimate the 15ε from water‐column profile and surface‐water samples collected in the Indian sector of the Southern Ocean—the first leg of the Antarctic Circumnavigation Expedition (December 2016–January 2017) and the Crossroads transect (April 2016). Consistent with prior work in the mid‐to‐late summer Southern Ocean, we estimate a higher 15ε (8.9 ± 0.6‰) for the northern Subantarctic Zone and a lower 15ε (5.4 ± 0.9‰) at and south of the Subantarctic Front. We interpret our data in the context of coincident measurements of phytoplankton community composition and estimates of iron and light stress. Similar to prior work, we find a significant, negative relationship between the 15ε and the average mixed‐layer photosynthetically active radiation flux of 30–100 μmol m−2 s−1, while above 100 μmol m−2 s−1, 15ε increases again. In addition, while we observe no robust relationship of the 15ε to iron availability or phytoplankton community, mixed‐layer nitrification over the Kerguelen Plateau appears to strongly influence its magnitude.

Modelling postfire recovery of snow albedo and forest structure to understand drivers of decades of reduced snow water storage and advanced snowmelt timing

AbstractForest fires darken snow albedo and degrade forest structure, ultimately reducing peak snow–water storage, and advancing snowmelt timing for up to 15 years following fire. To date, no volumetric estimates of watershed‐scale postfire effects on snow–water storage and snowmelt timing have been quantified over decades of postfire recovery. Using postfire parameterizations in a spatially‐distributed snow mass and energy balance model, SnowModel, we estimated postfire recovery of forest fire effects on snow–water equivalent (SWE) and snowmelt timing over decades following fire. Using this model, we quantified volumetric recovery of forest fire effects on snow hydrology across a chronosequence of eight sub‐alpine forests burned between 2000 and 2019 in the Triple Divide of western Wyoming. We found that immediately following fire, forest fire effects reduced snow–water storage by 6.8% (SD = 11.2%) and advanced the snow disappearance date by 31 days (SD = 9 days). Across the 15‐year recovery following fire, forest fire effects reduced snow–water storage by 4.5% (SD = 11.4%). Postfire effects on snow hydrology generally recovered over time, but still persisted beyond 15‐years following fire due to the observed postfire shift from forest to open meadow. Estimates of postfire reductions on peak SWE summed over the entire 15‐year postfire recovery period were 18 times greater than the immediate losses in the first winter following fire alone. These lasting effects of forest fires on snow hydrology decades following fire highlight the importance of postfire parameterizations for more accurate watershed‐scale volumetric estimates of forest fire effects on snow–water resources.

Leadership Style and Performance in Higher Education Institutions (PHEI): the Mediating Role of Organizational Culture

The purpose of this study is to examine the relationship between Leadership Style (LS) and Performance in Higher Education Institution (PHEI), the mediating effect of Organizational Culture (OC) on the relationship between the two is examined. Quantitative research approach with survey type. Descriptive and verifiable analyses were carried out to confirm and test the predictive causality of the constructed model. The total sample was 43 respondents selected using a purposive sampling approach. Path analysis is applied to analyze data in the research model. Forecasting with a regression approach, the process SPSS macro model was adopted to test the mediation effect. The estimation of the mediation effect was carried out by the Sobel test. The findings of the leadership style research have a positive and significant direct effect on the performance of higher education institutions, leadership style has a positive and significant effect on organizational culture, organizational culture has a positive and significant effect on the performance of higher education institutions. Organizational culture has a positive and significant indirect effect on the influence of leadership style on the performance of higher education institutions, there is a strong mediation effect of organizational culture compared to the direct effect of leadership style on the performance of higher education institutions. Research contributions, implications and recommendations are explained in this paper.

Joint structure learning and causal effect estimation for categorical graphical models.

The scope of this paper is a multivariate setting involving categorical variables. Following an external manipulation of one variable, the goal is to evaluate the causal effect on an outcome of interest. A typical scenario involves a system of variables representing lifestyle, physical and mental features, symptoms, and risk factors, with the outcome being the presence or absence of a disease. These variables are interconnected in complex ways, allowing the effect of an intervention to propagate through multiple paths. A distinctive feature of our approach is the estimation of causal effects while accounting for uncertainty in both the dependence structure, which we represent through a directed acyclic graph (DAG), and the DAG-model parameters. Specifically, we propose a Markov chain Monte Carlo algorithm that targets the joint posterior over DAGs and parameters, based on an efficient reversible-jump proposal scheme. We validate our method through extensive simulation studies and demonstrate that it outperforms current state-of-the-art procedures in terms of estimation accuracy. Finally, we apply our methodology to analyze a dataset on depression and anxiety in undergraduate students.

Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations.

Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregularly-spaced covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent multiply robust estimator and demonstrate analytically and in comprehensive simulation studies that it is more flexible and more efficient than the only alternative estimator proposed for the same setting. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counseling on alcohol consumption in American adolescents.

Heterosis and combining ability for phenological yield and fiber traits of cotton (Gossypium hirsutum L.) genotypes

The seeds of 4 lines, 3 testers, and their 12 F1 hybrids were sown in a randomized complete block design (RCBD) with 3 replications at Botanical Garden, Department of Plant Breeding and Genetics, Sindh Agriculture University, Tandojam during Kharif 2019. F1 hybrids were raised for the estimation of heterotic effects, GCA and SCA for days to 1st squaring, days to 1st boll formation Bolls opened at 90 days after sowing, Sympodial branches plant-1, Bolls plant-1, Boll weight (g), Seed cotton yield plant-1 (g), 100-seed weight (g), Ginning outturn (%) and staple length (mm). Genotypes, parents, crosses, parent's v/s crosses, lines, testers and line × testers were significant for days to 1st squaring and days to 1st boll forming. Bolls unveiled their fissures precisely 90 days post-sowing, while parameters including sympodial branches per plant, bolls per plant, individual boll weight, seed cotton yield per plant, 100-seed weight, ginning outturn, and staple length underwent meticulous evaluation. The statistical scrutiny unveiled the insignificance of both lines and testers concerning the 90-day boll dehiscence. Similarly, the parental influence on boll weight, 100-seed weight, and staple length exhibited a lack of statistical significance. Line CRIS-134, Sindh, and tester FH-901 gave a higher mean performance for all the traits. Among the hybrids, Mehran × FH-901, Sindh-1 × CIM-602, CRIS-134 × CIM-602, CRIS-342 × Koonj, and CRIS-342 × CIM-602 gave a desirable mean performance and heterotic effect for all the traits. CRIS-342 × Koonj produced maximum SCA effects for bolls per plant. Mehran × FH-901 recorded higher SCA effects for seed cotton yield plant-1 and 100-seed weight, CRIS-342 × CIM-602 for lint percentage, and CRIS-342 × FH-901 for staple length. It is suggested that these lines and testers should be utilized in the breeding program to improve these traits.

Estimation and analysis of the economic effects of the Corona Virus on Food Security in Iraq

The coronavirus pandemic is one of the types of communicable and infectious diseases that have caused very significant impacts in many economic variables, especially the food gap for wheat and rice crops in Iraq and in the majority of the world. The problem of research lies in the nature of the challenges and constraints to the production of these two crops, which has made the effects of the pandemic add additional burden to it. Iraq has thus lost its ability to achieve food security, while food prices have risen significantly, reflecting the high rate of deficits in the State's general budget, as Iraq is a net importer of most types of agricultural products, particularly the strategy. The research aims to study the reality of food security from wheat and rice crops during the period of the pandemic and its repercussions in the Iraqi economy. A number of findings have been reached in the practical aspect of the study, including the emergence of the inverse relationship between the quantities produced by wheat and its food gap. The relationship between the quantities produced and imported from rice and its food gap has been expulsive. The State's general budget deficit relationship with the food gap in both crops was expulsive. We recommend diversifying the production of the Iraqi economy, reducing dependence on the oil sector's foreign exchange revenues, adopting a policy of protection for domestic products from foreign competition and developing agricultural infrastructure to the extent that it is capable of achieving food security from both crops at all times.

Estimation of maternal and additive effect on reproduction and productive traits in Hardhenu cattle

AbstractThe study aimed to assess performance traits in Hardhenu cattle by analysing data from 445 animals born to 59 sires and 227 dams. The investigation focused on estimating (co)variance components and genetic parameters for reproduction and production traits in dairy cattle. Results from least‐squares analysis indicated a significant effect (p &lt; .01) of the period of calving (POC) on key production traits, including first lactation milk yield (FLMY), 300‐day milk yield (FLMY300), first peak yield (FPY) and total lactation milk yield (TLMY) in studied population. The least squares means for these traits were reported as follows: FLMY (2665.68 ± 45.66 kg), FLMY300 (2425.52 ± 34.41 kg), FLL (312.95 ± 3.83 days), FPY (11.52 ± 0.15 kg) and TLMY (9282.44 ± 167.03 kg) in Hardhenu cattle. In the studied population, only additive genetic variability was found to be present and there was absence of any significant maternal effect with respect to targeted traits in the resource population. Direct heritability estimates (h2) for FLMY, FLMY300, FLL, FPY, TLMY and other traits ranged from 0.03 to 0.41 in Hardhenu cattle. These findings offer valuable insights into the genetic factors influencing performance traits, contributing to the enhancement of breeding and management practices in Hardhenu cattle.

Calibrating doubly-robust estimators with unbalanced treatment assignment

Machine learning methods, particularly the double machine learning (DML) estimator (Chernozhukov et al., 2018), are increasingly popular for the estimation of the average treatment effect (ATE). However, datasets often exhibit unbalanced treatment assignments where only a few observations are treated, leading to unstable propensity score estimations. We propose a simple extension of the DML estimator which undersamples data for propensity score modeling and calibrates scores to match the original distribution. The paper provides theoretical results showing that the estimator retains the DML estimator’s asymptotic properties. A simulation study illustrates the finite sample performance of the estimator.

Propensity Weighted federated learning for treatment effect estimation in distributed imbalanced environments

Estimating treatment effects from observational data in medicine using causal inference is a very relevant task due to the abundance of observational data and the ethical and cost implications of conducting randomized experiments or experimental interventions. However, how could we estimate the effect of a treatment in a hospital that has very restricted access to treatment? In this paper, we want to address the problem of distributed causal inference, where hospitals not only have different distributions of patients, but also different treatment assignment criteria. Furthermore, it is necessary to take into account that due to privacy restrictions, personal patient data cannot be shared between hospitals. To address this problem, we propose an adaptation of the federated learning algorithm FederatedAveraging to one of the most advanced models for the prediction of treatment effects based on neural networks, TEDVAE. Our algorithm adaptation takes into account the shift in the treatment distribution between hospitals and is therefore called Propensity WeightedFederatedAveraging (PW FedAvg). As the distributions of the assignment of treatments become more unbalanced between the nodes, the estimation of causal effects becomes more challenging. The experiments show that PW FedAvg manages to reduce errors in the estimation of individual causal effects when imbalances are large, compared to VanillaFedAvg and other federated learning-based causal inference algorithms based on the application of federated learning to linear parametric models, Gaussian Processes and Random Fourier Features.

Nationwide frequency-dependent seismic site amplification models for Iceland

Seismic wave amplification due to localized site conditions or site-effects, is an important aspect of regional seismic hazard assessment. In the absence of systematic studies of frequency-dependent site-effects during strong Icelandic earthquakes, various local site proxies of large-scale studies in other seismic regions have been used and/or proposed for application in Iceland. Recently, however, earthquake site-effects were rigorously quantified for 34 strong-motion stations in Southwest Iceland for the first time and correlated to distinct Icelandic geological units of hard rock, rock, lava rock, and sedimentary soil. These units are prevalent throughout Iceland and in this study therefore, we present 1) nationwide maps of proxies (e.g. slope, Vs30, geological units) that may contribute to a better estimation of site effects and associated, 2) frequency-dependent site-amplification maps of Iceland relative to the median ground-motion prediction of Rahpeyma et al. (2023) [55]. We particularly focus on the two transform zones of the country, on the basis of digital elevation models and detailed geological maps. Specifically, the frequency-dependent site factors for each geological unit are presented at 1, 2, 5, 7, 10–30 Hz, and PGA. Finally, for comparison, we generate site amplification maps based on recent large-scale models developed in other seismic regions and/or applied in other studies (e.g., ESRM20) as well as various site proxies they are based on (e.g., geology- and slope-based inferred VS30, geomorphological sedimentary thickness). We compare site-proxy maps and amplification maps from both Icelandic and large-scale, non-Icelandic, models. Specifically, neither spatial patterns nor amplification levels in either proxy or amplification maps from large-scale non-Icelandic studies resemble those observed from local quantitative strong-motion research as presented in this study. We attribute the discrepancy primarily to the young geology of Iceland and its formation history. Additionally, this study compares model performance across frequencies by assessing the bias of model predictions against empirical site amplifications in the South Iceland Seismic Zone, accounting for site-to-site variability of residuals indicating the superior performance of the local amplification model. The results presented in this study now allow a more informed estimation of earthquake ground motion amplitudes on various geological units that are expected to result in more reliable and information-based seismic hazard estimates in Iceland.

Improving Outcomes After Post–Cardiac Arrest Brain Injury: A Scientific Statement From the International Liaison Committee on Resuscitation

This scientific statement presents a conceptual framework for the pathophysiology of post–cardiac arrest brain injury, explores reasons for previous failure to translate preclinical data to clinical practice, and outlines potential paths forward. Post–cardiac arrest brain injury is characterized by 4 distinct but overlapping phases: ischemic depolarization, reperfusion repolarization, dysregulation, and recovery and repair. Previous research has been challenging because of the limitations of laboratory models; heterogeneity in the patient populations enrolled; overoptimistic estimation of treatment effects leading to suboptimal sample sizes; timing and route of intervention delivery; limited or absent evidence that the intervention has engaged the mechanistic target; and heterogeneity in postresuscitation care, prognostication, and withdrawal of life-sustaining treatments. Future trials must tailor their interventions to the subset of patients most likely to benefit and deliver this intervention at the appropriate time, through the appropriate route, and at the appropriate dose. The complexity of post–cardiac arrest brain injury suggests that monotherapies are unlikely to be as successful as multimodal neuroprotective therapies. Biomarkers should be developed to identify patients with the targeted mechanism of injury, to quantify its severity, and to measure the response to therapy. Studies need to be adequately powered to detect effect sizes that are realistic and meaningful to patients, their families, and clinicians. Study designs should be optimized to accelerate the evaluation of the most promising interventions. Multidisciplinary and international collaboration will be essential to realize the goal of developing effective therapies for post–cardiac arrest brain injury.

A systematic survey of 200 systematic reviews with network meta-analysis (published 2020–2021) reveals that few reviews report structured evidence summaries

ObjectivesTo map whether and how systematic reviews (SRs) with network meta-analysis (NMA) use presentation formats to report (a) structured evidence summaries – here defined as reporting of effects estimates in absolute effects with certainty ratings and with a method to rate interventions across one or more outcome(s) – and (b) NMA results in general. Study Design and SettingWe conducted a systematic survey, searching MEDLINE (Ovid) for SRs with NMA published between January 1, 2020, and December 31, 2021. We planned to include a random sample of publications, with predefined mechanisms in place for saturation, and included SRs that met prespecified quality criteria and extracted data on presentation formats that reported: (a) estimates of effects, (b) certainty of the evidence, or (c) rating of interventions. ResultsThe 200 eligible SRs, from 158 unique Journals, utilized 1133 presentation formats. We found structured evidence summaries in 10 publications (5.0%), with 3 (1.5%) reporting structured evidence summaries across all outcomes, including benefits and harms. Sixteen of the 133 SRs (11.7%) reporting dichotomous outcomes included estimates of absolute effects. Seventy-six SRs (38.0%) reported both benefits and harms and 26 SRs (13.0%) reported certainty ratings in presentation formats, 20 (76.9%) used Grading of Recommendations Assessment, Development and Evaluation and 6 (23.1%) used Confidence In Network Meta-analysis. Surface Under the Cumulative Ranking Curve was the most common method to rate interventions (69 SRs, 34.5%). NMA results were most often reported using forest plots (108 SRs, 54.0%) and league tables (93 SRs, 46.5%). ConclusionMost SRs with NMA do not report structured evidence summaries and only rarely do such summaries include reporting of both benefits and harms; those that do offer effective user-friendly communication and provide models for optimal NMA presentation practice.

Nuclear deformation effects in the spectra of highly charged ions

With the increasing precision of spectroscopic measurements, there is a growing demand for more accurate theoretical predictions, which requires the estimation of various higher-order effects, such as the nuclear deformation effect. Here, we present the results for nuclear deformation correction for the widest range of nuclei. The effects are investigated in terms of electronic transition energies, g factors, and hyperfine splitting constants, by implementing the deformed Fermi nuclear model to the Dirac equation. Based on the improved methodology and appropriate data classification, we present the results for over 1100 nuclei on figures, showing the general trends and anomalies. Moreover, it can serve as a simple tool providing estimations for specific planned research. In addition, we examine the connections and significance of deformation effects in the search of new physics with singly charged ions. Published by the American Physical Society 2024