Background: Nomogram, a visual clinical predictive model, provides a scientific basis for clinical decision making. Herein, we investigated 20 years of nomogram research responses, focusing on current and future trends and analytical challenges.Methods: We mined data of scientific literature from the Core Collection of Web of Science, searching for the original articles with title “Nomogram^*/Parton Table^*/Parton Nomogram^*”, published within January 1st, 2000 to December 30th, 2021. Data records were validated using HistCite Version and analyzed with a transformable statistical method, the Bibliometrix 3.0 package of R Studio.Results: In total, 4,176 original articles written by 19,158 authors were included from 915 sources. Annually, Nomogram publications are continually produced, which have rapidly grown since 2018. China published the most articles; however, its total citations ranked second after the United States. Both total citations and average article citations in the United States rank first globally, and a high degree of cooperation exists between countries. Frontiers in Oncology published the most papers (238); this number has grown rapidly since 2019. Journal of Urology had the highest H-index, with an average increase in publications over the past 20 years. Most research topics were tumor-related, among which tumor risk prediction and prognostic evaluation were the main contents. Research on prognostic assessment is more published and advanced, while risk prediction and diagnosis have good developmental prospects. Furthermore, nomogram of the urinary system has been highly developed. Following advancements in nomogram modeling, it has recently been applied to non-oncological subjects.Conclusion: This bibliometric analysis provides a comprehensive overview of the current nomogram status, which could enable better understanding of its development over the years, and provide global researchers a comprehensive analysis and structured information to help identify hot spots and gaps in future research.

NFKB1, a core transcription factor critical in various biological process (BP), is increasingly studied for its role in tumors. This research combines literature reviews, meta-analyses, and bioinformatics to systematically explore NFKB1's involvement in tumor initiation and progression. A unique focus is placed on the NFKB1-94 ATTG promoter polymorphism, highlighting its association with cancer risk across diverse genetic models and ethnic groups, alongside comprehensive analysis of pan-cancer expression patterns and drug sensitivity. The study reveals the intricate connections between NFKB1 and tumors, highlighting its significant roles in invasion, metastasis, genomic stability, and metabolic changes. Through meta-analysis, it is evidenced that tumor specimens exhibit increased NFKB1 expression when compared to non-tumor specimens, although its association with cancer incidence requires further investigation. Analysis from the Gene Expression Omnibus (GEO) database suggests that high NFKB1 gene expression may not markedly impact tumor patient prognosis. The noticeable correlation between the NFKB1-94 ATTG promoter polymorphic sequence and elevated cancer susceptibility is highlighted across different genetic models. Furthermore, bioinformatics analysis uncovers NFKB1's association with the sensitivity to various anticancer drugs and its central involvement in crucial BP like the cell cycle, cytoskeleton assembly, and cellular senescence. Overall, NFKB1's expression and polymorphisms are significantly linked to tumor risk, prognosis, and treatment response, highlighting its prospect as a forthcoming aim for cancer treatment. This study offers a robust foundation for further exploration of NFKB1's mechanisms and the development of innovative therapeutic strategies.

The purpose of this study was to evaluate the international scientific output on postpartum depression (PPD) research during 2000–2020 through a bibliometric analysis and to explore research hotspots, frontiers, and trends in the field of postpartum depression. We searched the Web of Science Core Collection for publications on postpartum depression published between 2000 and 2020. CiteSpace, gCluto, and other software applications were used to analyze the data by year, journal, and country. A total of 2,963 publications were retrieved and 96 countries or regions published related papers. The United States had the largest number of published papers and the highest betweenness centrality, which is the dominant position in the field of postpartum depression. A total of 717 journals published papers, with the Archives of Womens Mental Health ranked first in terms of volume and betweenness centrality. In this study, 31 high-frequency main MeSH terms/subheadings were selected. The high-frequency MeSH terms were clustered into six categories: an overview of depression-related research, diagnostic and screening scales for postpartum depression, epidemiological investigation into postpartum depression, treatment and drug selection for postpartum depression, psychological research on postpartum depression, and etiology, physiopathology, complications, genetics of postpartum depression. Finally, we used strategic diagram to analyze research trends in postpartum depression. This study has identified a continuous significant increase in the publication of PPD articles. Currently, the etiology, physiological pathology, intervention and treatment of complications on PPD are immature, which provides reference for the trend of obstetric psychology.

Background: Ovarian cancer is a common gynecological malignancy, most of which is epithelial ovarian cancer (EOC). Advanced EOC is linked with a higher incidence of premature death. To date, no effective prognostic tools are available to evaluate the possibility of early death in patients with advanced EOC.Methods: Advanced (FIGO stage III and IV) EOC patients who were enrolled in the Surveillance, Epidemiology, and End Results database between 2004 and 2015 were regarded as subjects and studied. We aimed to construct a nomogram that can deliver early death prognosis in patients with advanced EOC by identifying crucial independent factors using univariate and multivariate logistic regression analyses to help deliver accurate prognoses.Results: In total, 13,403 patients with advanced EOC were included in this study. Three hundred ninety-seven out of a total of 9,379 FIGO stage III patients died early. There were 4,024 patients with FIGO stage IV, 414 of whom died early. Nomograms based on independent prognostic factors have the satisfactory predictive capability and clinical pragmatism. The internal validation feature of the nomogram demonstrated a high level of accuracy of the predicted death.Conclusions: By analyzing data from a large cohort, a clinically convenient nomogram was established to predict premature death in advanced EOC. This tool can aid clinicians in screening patients who are at higher risk for tailoring treatment plans.

This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk assessment and prevention in clinical settings. We conducted a retrospective multicenter cohort study in Northeast China, including women who had vaginal deliveries at three tertiary hospitals from September 2018 to December 2023. Data were extracted from electronic medical records. The dataset was split into a training set (70%) and an internal validation set (30%) to prevent overfitting. External validation was performed on a separate dataset. Several evaluation metrics, including the area under the receiver operating characteristic curve (AUC), were used to compare prediction performance. Features were ranked using SHAP, and the final model was explained. The XGBoost model demonstrated superior predictive accuracy for PPH, with an AUC of 0.997 in the training set. SHAP value-based feature selection identified 15 key features contributing to the model’s predictive power. SHAP dependence and summary plots provided intuitive insights into each feature’s contribution, enabling the identification of anomalies. The final model maintained high predictive power, with an AUC of 0.894 in internal validation and 0.880 in external validation. This study successfully developed an interpretable ML model that predicts PPH with high accuracy. Future studies with larger and more diverse datasets are necessary to further validate and refine the model, particularly to assess its generalizability across different populations and healthcare settings.