Clinical Deintensification
Cross-source consensus on Clinical Deintensification from 1 sources and 5 claims.
1 sources · 5 claims
Uses
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Other
Highlighted claims
- The findings suggest a gap between evidence-based recommendations and real-world prescribing. — Estimating prevalence and predictors of glucose-lowering overtreatment among older adults with type 2 diabetes in long-term care and community settings: a machine learning–based cohort study
- Guidance includes deprescribing and deintensification for some frail and older patients, but intensive glucose-lowering treatment often continues despite controlled A1C. — Estimating prevalence and predictors of glucose-lowering overtreatment among older adults with type 2 diabetes in long-term care and community settings: a machine learning–based cohort study
- Persistent intensive treatment despite controlled A1C indicates a need for stronger implementation of deintensification initiatives. — Estimating prevalence and predictors of glucose-lowering overtreatment among older adults with type 2 diabetes in long-term care and community settings: a machine learning–based cohort study
- Predictive analytics could identify patients who may benefit from deintensification and support quality improvement initiatives. — Estimating prevalence and predictors of glucose-lowering overtreatment among older adults with type 2 diabetes in long-term care and community settings: a machine learning–based cohort study
- Simpler transparent models may be preferable in clinical settings because they are easier to implement and may be more trusted by clinicians. — Estimating prevalence and predictors of glucose-lowering overtreatment among older adults with type 2 diabetes in long-term care and community settings: a machine learning–based cohort study