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• W2066133032 abstract "Prediction models for the identification of Lynch syndrome have been developed to quantify an individual’s risk of carrying a mismatch repair gene mutation and help clinicians decide for whom further risk assessment and genetic testing is necessary. There are diverse clinical settings in which a healthcare provider has the opportunity to assess an individual for Lynch syndrome. Prediction models offer a potentially feasible and useful strategy to systematically identify at-risk individuals, whether they are affected with colorectal cancer or not, and to help with management of the implications of molecular and germline test results. Given the complexity of diagnostic information currently available to clinicians involved in identifying and caring for patients with Lynch syndrome, prediction models provide a useful and complementary aid in medical decision-making. Systematic implementation of prediction models estimates should be considered in routine clinical care and at various stages of cancer risk assessment and prevention. In this manuscript, we review the main prediction models developed for Lynch syndrome, focus on their specific features and performance assessed in several validation studies, compare the models with other clinical and molecular strategies for the diagnosis of Lynch syndrome, and discuss their potential uses in clinical practice." @default.
• W2066133032 created "2016-06-24" @default.
• W2066133032 creator A5034561048 @default.
• W2066133032 creator A5079620607 @default.
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• W2066133032 date "2013-04-04" @default.
• W2066133032 modified "2023-10-18" @default.
• W2066133032 title "Prediction models in Lynch syndrome" @default.
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• W2066133032 doi "https://doi.org/10.1007/s10689-013-9632-0" @default.
• W2066133032 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3757552" @default.
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