ObjectiveTo discuss the effect of synthesized physical nursing care on the treatment of meibomian gland dysfunction (MGD) patients. MethodWe collected 100 MGD patients between March and September 2014, and randomized them into conventional and synthesized physical nursing care groups with 50 patients in each. We evaluated the subjective symptoms, the break-up time of tear-film and the function of lacrimal gland secretion before and after treatment. We surveyed the satisfaction of these patients during the follow-up. ResultsCompared with the conventional nursing care group, the synthesized group had less clinical symptoms, longer break-up times of tear-film and better lacrimal gland secretion function. The results showed remarkable statistical significance (P<0.05). ConclusionsSynthesized nursing care has significant effect on the treatment efficacy of MGD outpatients.
Traditional Chinese medicine (TCM) emphasizes syndrome differentiation and individualized interventions. However, in real-world clinical studies, complex confounding and the unobservability of counterfactual outcomes pose major challenges for evaluating individualized treatment effects. Causal machine learning, grounded in the potential outcomes framework, provides a flexible, nonparametric approach to model treatment assignment and outcome distributions. By adjusting for baseline and time-varying confounding, it enables the generation of patient-specific counterfactual outcomes and more accurate estimation of causal effects at the individual level. This methodological advance addresses the limitations of conventional approaches that focus mainly on average treatment effects. Taking advanced colorectal cancer as a case example, we demonstrate how causal machine learning can identify "which patients benefit most from which treatment?" thereby supporting personalized clinical decision-making and optimization of therapeutic strategies in TCM. Overall, causal machine learning holds both methodological significance and direct clinical value for individualized efficacy research in TCM.