SynthesisWorld journal of surgical oncology2025
Risk prediction models for permanence of temporary stoma after radical surgery of rectal cancer: a systematic review.
Synthesis in World journal of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
9 authors.
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Abstract
objectiveTo methodologically assess the prediction model for temporary stoma permanence in patients with rectal cancer and provide evidence-based guidance for the construction and clinical application of related models.
methodsFrom launch to January 3, 2025, computer searches were performed in nine databases. Two researchers independently searched the literature, and the Critical Appraisal and Data Extraction Checklist for Systematic Evaluation of Predictive Modelling was used to extract data. The Predictive Modelling Research Risk of Bias Assessment Tool was used to assess the studies' applicability and risk of bias.
resultsNine studies were incorporated, exhibiting AUC/C-index values between 0.612 and 0.942, signifying good predictive efficacy in some models. Nonetheless, the included studies showed restricted applicability and a high risk of bias, especially regarding the selection of research populations and data analysis. The predominant determinants across models encompassed T-stage, neoadjuvant chemoradiotherapy, American Society of Anesthesiologists score, carcinoembryonic antigen level, distant metastasis, lymph node metastasis, anastomotic leakage, and age.
conclusionCurrent prediction models for temporary stoma permanence in rectal cancer patients exhibit significant limitations. In order to improve the accuracy of clinical predictions and inform clinical decision-making, future research should improve study design and reporting standards, as well as build and verify a prediction model that is highly applicable to real-world clinical demands and has a low risk of bias.
trial registrationPROSPERO: CRD420250637947.
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