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38 records · Page 3Linked to original sources
PubMed2025

Worldwide prevalence of haemorrhoids: a systematic review and meta-analysis.

BACKGROUND: Haemorrhoidal disease (HD) is one of the most common anorectal disorders globally, significantly impacting individuals' quality of life and productivity. Despite its importance, global prevalence remains unclear due to limited population-specific studies. This study aimed to systematically assess the global prevalence of HD through a systematic review and meta-analysis. METHODS: We conducted a systematic review and meta-analysis by searching PubMed, Scopus, Embase, Web of Science, and Google Scholar up to March 31, 2025, without language restrictions. Studies reporting prevalence of haemorrhoids in general, clinical, or high-risk populations were included. Exclusion criteria comprised studies lacking total sample size, focusing on other anorectal conditions, or using duplicate or insufficient data. Four independent reviewers extracted and appraised study quality using the Joanna Briggs Institute tool. The primary outcome was pooled point prevalence of HD, analyzed using a random-effects model with 95% confidence intervals (CIs). The study was registered in PROSPERO (CRD420251045600). RESULTS: From 6,312 records, 150 studies (210 datasets) comprising 8,960,338 individuals were included. The global pooled point prevalence was 25.92% (95% CI: 22.62-29.22). Lifetime prevalence was 27.19% (95% CI: 14.77-39.60), and one-year prevalence was 21.65% (95% CI: 14.33-28.97). Prevalence was higher in women (27.33%, 95% CI: 21.84-32.82) than in men, and highest in the African region 28.07% (95% CI: 15.34-40.79). Invasive diagnostic methods (28.05%, 95% CI: 23.86-32.26) yielded higher prevalence estimates than non-invasive methods. Also, factors showing associations with HD in unadjusted analyses include older age, obesity, pregnancy, diabetes, family history, constipation, and hypertension. CONCLUSION: HD remains a prevalent condition globally, with minor variation across regions. The burden is consistent regardless of socioeconomic context. Diagnostic method and population characteristics influence prevalence estimates. These findings underscore the importance of targeted prevention and early intervention strategies, especially for at-risk groups.

Humans↗
PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗