UNASSIGNED: The reliability of clinical evidence depends on high-quality meta-analyses/ systematic reviews (MAs/SRs). However, there has been no assessment of the quality of MAs/SRs for repetitive transcranial magnetic stimulation (rTMS) in post-stroke cognitive impairment (PSCI), both nationally and internationally. This article seeks to use radar plotting to visually present the quality of MAs/SRs on rTMS for improving cognitive function in PSCI, aiming to offer an intuitive foundation for clinical research.
UNASSIGNED: Eight Chinese or English databases were systematically searched to collect comprehensive literature, and the retrieval time ranged from inception to 26 March 2024. Literature ranking was calculated using six dimensions: publication year, design type, AMSTAR-2 score, PRISMA score, publication bias, and homogeneity. Finally, radar plots were drafted to present a multivariate literature evaluation. The GRADE tool assessed the strength of evidence for the outcome indicators included in the MAs/SRs.
UNASSIGNED: The 17 articles included had average scores of 12.29, 17, 9.88, 9.71, 12.88, and 12.76 for each dimension. The radar plot showed that an article published in 2023 had the highest rank and a large radar plot area, while an article published in 2021 had the lowest rank and a small radar plot area. The GRADE tool evaluation revealed that 51 pieces of evidence were of very low quality, 67 were of low quality, 12 were of moderate quality, and only one was of high quality.
UNASSIGNED: The average rank score of literature ranged from 8.50 to 17, with higher rankings indicating greater significance in literature reference. Variations in literature quality were attributed to inadequate study planning, irregular literature search and screening, insufficient description of inclusion criteria for studies, and inadequate consideration of bias risk in the included studies. Most MAs/SRs indicated that rTMS was more effective than the control group in enhancing the global cognitive function and activities of daily living in PSCI patients. However, the overall quality of the literature was generally low and needs validation from future high-quality evidence.Systematic review registration:https://www.crd.york.ac.uk/prospero/, identifier CRD42023491280.

Fundamental data analysis assists in the evaluation of critical questions to discern essential facts and elicit formerly invisible evidence. In this article, we provide clarity into a subtle phenomenon observed in cancer incidences throughout the time of the COVID-19 pandemic. We analyzed the cancer incidence data from the American Cancer Society [1]. We partitioned the data into three groups: the pre-COVID-19 years (2017, 2018), during the COVID-19 years (2019, 2020, 2021), and the post-COVID-19 years (2022, 2023). In a novel manner, we applied principal components analysis (PCA), computed the angles between the cancer incidence vectors, and then added lognormal probability concepts in our analysis. Our analytic results revealed that the cancer incidences shifted within each era (pre, during, and post), with a meaningful change in the cancer incidences occurring in 2020, the peak of the COVID-19 era. We defined, computed, and interpreted the exceedance probability for a cancer type to have 1000 incidences in a future year among the breast, cervical, colorectal, uterine corpus, leukemia, lung & bronchus, melanoma, Hodgkin\'s lymphoma, prostate, and urinary cancers. We also defined, estimated, and illustrated indices for other cancer diagnoses from the vantage point of breast cancer in pre, during, and post-COVID-19 eras. The angle vectors post the COVID-19 were 72% less than pre-pandemic and 28% less than during the pandemic. The movement of cancer vectors was dynamic between these eras, and movement greatly differed by type of cancer. A trend chart of cervical cancer showed statistical anomalies in the years 2019 and 2021. Based on our findings, a few future research directions are pointed out.

The spectrum of benign B-cell precursors, known as hematogones (HGs), shows a significant morphological and immunophenotypic overlap with their malignant counterpart i.e. B-lymphoid blasts (BLBs). This results in a diagnostic dilemma in assessment of cases wherein there is a physiological preponderance of HGs and also poses a significant challenge in measurable residual disease assessment in B-cell acute lymphoblastic leukaemia. Consequently, expression patterns of various immunophenotypic markers are considered the most important tool in identification and delineation of HGs from BLBs. However, certain aspects of B-cell compartment evaluation by flow cytometric immunophenotyping and its relevance in clinical scenarios is yet to be defined precisely. This review summarizes current flowcytometric data on HGs and its discrimination from BLBs based on thorough review of literature and evaluation of in-house data. Furthermore, it focuses on the utility of an additional analytical tool i.e., radar plot for a comprehensive representation of various subsets of the B-cell compartment and their differentiation from BLBs.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s12288-023-01696-5.

The approach introduced by Network Physiology intends to find and quantify connectedness between close- and far related aspects of a person\'s Physiome. In this study I applied a Network-inspired analysis to a set of measurement data that had been assembled to detect prospective orthostatic intolerant subjects among people who were destined to go into Space for a two weeks mission. The advantage of this approach being that it is essentially model-free: no complex physiological model is required to interpret the data. This type of analysis is essentially applicable to many datasets where individuals must be found that \"stand out from the crowd\". The dataset consists of physiological variables measured in 22 participants (4f/18 m; 12 prospective astronauts/cosmonauts, 10 healthy controls), in supine, + 30° and + 70° upright tilted positions. Steady state values of finger blood pressure and derived thereof: mean arterial pressure, heart rate, stroke volume, cardiac output, systemic vascular resistance; middle cerebral artery blood flow velocity and end-tidal pCO2 in tilted position were (%)-normalized for each participant to the supine position. This yielded averaged responses for each variable, with statistical spread. All variables i.e., the \"average person\'s response\" and a set of %-values defining each participant are presented as radar plots to make each ensemble transparent. Multivariate analysis for all values resulted in obvious dependencies and some unexpected ones. Most interesting is how individual participants maintained their blood pressure and brain blood flow. In fact, 13/22 participants had all normalized Δ-values (i.e., the deviation from the group average, normalized for the standard deviation), both for +30° and +70°, within the 95% range. The remaining group demonstrated miscellaneous response patterns, with one or more larger Δ-values, however of no consequence for orthostasis. The values from one prospective cosmonaut stood out as suspect. However, early morning standing blood pressure within 12 h after return to Earth (without volume repletion) demonstrated no syncope. This study demonstrates an integrative way to model-free assess a large dataset, applying multivariate analysis and common sense derived from textbook physiology.

Acute promyelocytic leukemia (APL) is one of the most life-threatening hematological emergencies and requires a prompt correct diagnosis by cytomorphology and flow cytometry (FCM) with later confirmation by cytogenetics/molecular genetics. However, nucleophosmin 1 muted acute myeloid leukemia (NPM1+ AML) can mimic APL, especially the hypogranular variant of APL. Our study aimed to develop a novel, Radar plot-based FCM strategy to distinguish APLs and NPM1+ AMLs quickly and accurately.
Diagnostic samples from 52 APL and 32 NPM1+ AMLs patients were analyzed by a 3-tube panel of 10-color FCM. Radar plots combining all markers were constructed for each tube. Percentages of positive leukemic cells and mean fluorescence intensity were calculated for all the markers.
APL showed significantly higher expression of CD64, CD2, and CD13, whereas more leukemic cells were positive for CD11b, CD11c, CD15, CD36, and HLA-DR in NPM1+ AMLs. Radar plots featured CD2 expression, a lack of a monocytic component, lack of expression of HLA-DR and CD15, and a lack of a prominent CD11c+ population as recurring characteristics of APL. The presence of blasts with low SSC, presence of at least some monocytes, some expression of HLA-DR and/or CD15, and a prominent CD11c population were recurrent characteristics of NPM1+ AMLs. Radar plot analysis could confidently separate all hypergranular APL cases from any NPM1+ AML and in 90% of cases between variant APL and blastic NPM1+ AML.
Radar plots can potentially add to differential diagnostics as they exhibit characteristic patterns distinguishing APL and different types of NPM1+ AMLs.

Network meta-analysis (NMA) simultaneously synthesises direct and indirect evidence on the relative efficacy and safety of at least three treatments. A decision maker may use the coherent results of an NMA to determine which treatment is best for a given outcome. However, this evidence must be balanced across multiple outcomes. This study aims to provide a framework that permits the objective integration of the comparative effectiveness and safety of treatments across multiple outcomes.
In the proposed framework, measures of each treatment\'s performance are plotted on its own pie chart, superimposed on another pie chart representing the performance of a hypothetical treatment that is the best across all outcomes. This creates a spie chart for each treatment, where the coverage area represents the probability a treatment ranks best overall. The angles of each sector may be adjusted to reflect the importance of each outcome to a decision maker. The framework is illustrated using two published NMA datasets comparing dietary oils and fats and psoriasis treatments. Outcome measures are plotted in terms of the surface under the cumulative ranking curve. The use of the spie chart was contrasted with that of the radar plot.
In the NMA comparing the effects of dietary oils and fats on four lipid biomarkers, the ease of incorporating the lipids\' relative importance on spie charts was demonstrated using coefficients from a published risk prediction model on coronary heart disease. Radar plots produced two sets of areas based on the ordering of the lipids on the axes, while the spie chart only produced one set. In the NMA comparing psoriasis treatments, the areas inside spie charts containing both efficacy and safety outcomes masked critical information on the treatments\' comparative safety. Plotting the areas inside spie charts of the efficacy outcomes against measures of the safety outcome facilitated simultaneous comparisons of the treatments\' benefits and harms.
The spie chart is more optimal than a radar plot for integrating the comparative effectiveness or safety of a treatment across multiple outcomes. Formal validation in the decision-making context, along with statistical comparisons with other recent approaches are required.

Aim: The radar plot is a relatively new way of communicating blood glucose monitoring system (BGMS) accuracy and precision: data points positioned within concentric circles represent the magnitude (increasing with distance from center) and direction (relative to horizontal) of BGMS-error (center = equivalency with reference instrument measurement). This manuscript aims to demonstrate the utility of radar plots as visual tools for interpretation of BGMS analytical performance. Results & methodology: Radar plots were constructed for five BGMSs, to compare BGMS blood glucose results with reference instrument measurements. Conclusion: Radar plots are a useful tool for the visualization of BGMS analytical performance, communicating accuracy, precision and the satisfaction of certain regulatory criteria at a glance.

To investigate, in a sample of probable sleep bruxers with and without temporomandibular disorder (TMD) pain, the presence and relationships between clinical jaw-muscle symptoms, and test their associations with jaw-muscle electromyographic (EMG) activity during sleep.
Pain, unpleasantness, tiredness, tension, soreness, and stiffness were scored on a 0-10 numerical rating scale (NRS) in 50 probable sleep bruxers. The sample was subdivided into two groups, i.e., with and without TMD pain. Multiple-night, single-channel EMG recordings were performed. Descriptive data, correlations between the six symptoms, and correlations between symptoms and EMG measures, i.e. EMG events/recording, EMG events/hour, and night-to-night variability in EMG events, were calculated.
In the total sample, 90% of the participants reported at least one symptom. Tiredness and tension were the most prevalent symptoms (both 78%), and pain the least (30%). In the TMD pain group, pain remained the least reported symptom (57%). Intensity of symptoms was low to moderate, with tension presenting the highest median in the total sample (NRS 4), the TMD pain group (NRS 5), and non-TMD group (NRS 3). Significant correlations between all symptoms were found in the total sample, but not in the two subgroups. No significant associations between EMG measures and muscle symptoms emerged.
Jaw-muscle symptoms other than pain were highly prevalent in a sample of probable sleep bruxers. There were no associations between these symptoms and EMG measures of jaw-muscle activity during sleep. These findings challenge the concept of simple relationships between jaw-muscle activity during sleep and clinical muscle symptoms.

Common, enigmatic musculoskeletal conditions such as whiplash-associated disorder, myofascial pain syndrome, low back pain, headache, fibromyalgia, osteoarthritis, and rotator cuff pathology, account for significant social, economic, and personal burdens on a global scale. Despite their primacy (and shared sequelae) there remains a paucity of available and effective management options for patients with both acute and chronic conditions. Establishing an accurate prognostic or diagnostic profile on a patient-by-patient basis can challenge the insight of both novice and expert clinicians. Questions remain on how and when to choose the right tool(s), at the right time(s), for the right patient(s), for the right problem(s). The aim of this paper is to introduce a new clinical reasoning framework that is simple in presentation but allows interpretation of complex clinical patterns, and is adaptable across patient populations with acute or chronic, traumatic or non-traumatic pain. The concepts of clinical phenotyping (e.g. identifying observable characteristics of an individual resulting from the interaction of his/her genotype and their environment) and triangulation serve as the foundation for this framework. Based on our own clinical and research programs, we present these concepts using two patient cases; a) whiplash-associated disorder (WAD) following a motor vehicle collision and b) mechanical low back pain.

Graphical presentation of blood glucose monitoring systems\' (BGMSs) accuracy typically includes difference plots (DPs). Recently, 3 new approaches were presented: radar plots (RPs), rectangle target plots (RTPs), and surveillance error grids (SEGs). BGMS data were modeled based on 3 scenarios that can be encountered in real life to highlight strengths and limitations of these approaches. Detailed assessment of BGMS data may be easier in plots with individual data points (DPs, RPs, SEGs), whereas RTPs may facilitate display of large amounts of data or comparison of BGMS. SEGs have the advantage of assessing clinical risk. The selection of a specific type depends mostly on the kind of information sought (eg, accuracy in specific concentration intervals, lot-to-lot variability, clinical risk) as there is no \"absolute best\" approach.