SynthesisSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2026
Diagnostic predictive models for cancer-related fatigue: current evidence and future directions.
Synthesis in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2026. 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
6 authors.
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Abstract
purposeTo evaluate the performance and methodological quality of published diagnostic prediction models for cancer-related fatigue (CRF), and to provide evidence for clinical practice and future research.
methodsA systematic review and meta-analysis were conducted. PubMed, Web of Science, the Cochrane Library, Embase, and Scopus were searched from inception to October 18, 2024, for studies developing or validating diagnostic prediction models for CRF. The pooled area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI) were calculated using R. Heterogeneity was assessed using the I
resultsA total of 8418 records were identified, of which 13 studies met the inclusion criteria. These studies included 23 cohorts, 444,447 cancer patients, and 11 diagnostic prediction models. The pooled AUC was 0.83 (95% CI = 0.78-0.87), indicating moderate-to-good discrimination. However, substantial heterogeneity was observed (I
conclusionExisting CRF diagnostic prediction models show moderate-to-good discrimination in research settings, but their performance varies across populations, outcome definitions, and modeling approaches. Future studies should prioritize large-scale, multi-center, multiethnic, and externally validated models to improve early identification and precise management of CRF.
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