R statistical software version 4

Descriptive statistics including the minimum, maximum, mean, standard deviation, and coefficient of variation were analyzed using Web Agri Stat Package-2 (WASP-2) developed by ICAR Complex Goa, India. Qualitative data was exposed to non-parametric Spearman correlations, whereas quantitative data was submitted to parametric Pearson correlations. Principal component Analysis (PCA) and K-mean cluster plots were used to analyze data. Data sets may be divided into K clusters, which are represented by their centroids, using the K-means clustering technique (31 (link)).
A dendrogram was created by using both qualitative and quantitative traits. Based on Ward's approach and Euclidean distance, respectively, aggregative hierarchical clustering (AHC) and genetic dissimilarity component analysis were performed.
The R statistical software (version 4.2.0; The R Foundation) was used to analyze the reported data for correlation, PCA, K-mean cluster plots, and dendrogram of 33 attributes from the 28 guava germplasm.

Categorical variables are presented as numbers and percentages, while continuous variables are presented as median and first and third quartiles (Q1 and Q3). Differences between groups were assessed using the Mann–Whitney U test or Fisher exact test depending on the data type. To investigate the association between age, gender, complexity, pre-existing heart failure, arterial hypertension, chronic lung diseases, and 30-day mortality rate, multivariable logistic regression analysis was performed, and the findings were summarized as odds ratios (OR) with respective 95% confidence intervals (CI). All analyses were explorative, and a 2-sided p-value of less than 0.05 was considered significant throughout the study. All analyses were performed using the R statistical software Version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria).

Survival analysis was performed to investigate the impact of the 1-year decline in DLCO ≥ 10% predicted on mortality outcome. The index date was designated as the date of the 1-year follow-up PFT. The time to death or lung transplantation was compared between the two groups (stable DLCO vs. decline in DLCO) using the Kaplan–Meier method and the log-rank test. The Cox proportional hazards regression model was used for multivariable analysis to adjust for other confounding factors considered clinically significant, and adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) were calculated. Age, sex, baseline lung function (FVC and DLCO), baseline CT characteristics (extent of reticulation and honeycombing), and 1-year changes in FVC were selected as confounding factors.
Furthermore, we conducted a subgroup analysis according to the 1-year changes in FVC, baseline GAP stage, extent of emphysema, and pulmonary artery-to-aorta ratio to determine whether the prognostic impact of a decline in DLCO was robust. All statistical analyses were performed using the R statistical software version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria) and STATA software (version 17.0; StataCorp LP, College Station, TX, USA).

Cox proportional hazards regression and piecewise linear regression [30 (link)] were used to evaluate the relationship between FI and mortality, and the Kaplan–Meier survival function curve was used to estimate the seven-year survival in relation to the FI and frailty status. The areas under the receiver-operating characteristic (ROC) curves (AUCs) of FI and frailty status were calculated to compare the effects of these parameters on death outcomes during the follow-up period. Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA), IBM SPSS Statistics version 20 (SPSS Inc., Chicago, IL, USA), and R statistical software version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria).

We first calculated descriptive statistics including mean (SD) for continuous measures and counts and percentages for categorical data. χ² and Fisher exact tests were used to test differences between health care facilities due to small cell counts. Paired sample t tests were used for normally distributed continuous variables. Nonparametric tests such as the Wilcoxon signed rank test were applied to variables that were not normally distributed. Further, Cohen d effect size was evaluated for each covariate. Repeated measures analysis of variance was used to analyze the changes over time (prewar vs wartime) in hospital-related facilities. Before analysis, multicollinearity was assessed for each covariate (tolerance statistic < 0.4). No evidence of multicollinearity was observed so all variables were retained. Statistical significance was defined based on a 2-sided standard alpha level of .05. R statistical software, version 4.3.1 (R Project for Statistical Computing), was used to perform all the statistical analysis.

Categorical variables are expressed as frequencies and percentages, and chi-square tests or Fisher’s exact tests were performed as appropriate. For data following a normal distribution, continuous variables were presented as the mean ± standard deviation (SD), and data were analyzed by using Student’s t-test. For data that did not follow a normal distribution, the continuous variables were represented in the form of median and interquartile intervals (IQR), and the Mann-Whitney U test was used to analyze the data. To assess the agreement of the ratings between the two reviewers, Cohen kappa coefficients were calculated. The Spearman test was used to evaluate the correlation between different scores and video features. All the statistical analyses were performed with R statistical software version 4.3.1 (www.r-project.org), and a two-tailed P value < 0.05 was considered to indicate statistical significance.

The bibliometric analysis descriptively summarizes the number of COVID-19–related highly cited studies. We counted the number of highly cited studies based on the fractional counting method. Compared with the full counting method, which counts the full number of each co-author and institutional affiliation, the fractional counting method had a fractional weight of each co-author and institutional affiliation, and each publication had a total weight of 1.^{14 (link)} Highly cited study counts were compared between research fields, countries, and affiliations. As a sensitivity analysis, we performed the full counting method instead of the fractional counting method. We also performed the fractional counting method on countries and affiliations of corresponding authors as a sensitivity analysis. Data were analyzed using R statistical software version 4.3.1 (R Project for Statistical Computing). Data were analyzed from January through July 2023.