개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
Objectives: To develop a standardized diagnostic pattern identification equation for stroke patients, our group conducted a study to derive the predictive logistic equations. However, the sample size was relatively small. In the current study, we aimed to derive new predictive logistic equations for each diagnostic pattern using an expanded number of subjects.
Methods: This study was a hospital-based multi-center trial recruited stroke patients within 30 days of symptom onset. Patients’ general information, and the variables related to diagnostic pattern identification were measured. The diagnostic pattern of each patient was identified independently by two Korean Medicine Doctors. To derive a predictive model for pattern identification, binary logistic regression analysis was applied.
Results: Among the 1,251 patients, 385 patients (30.8%) had the Fire Heat Pattern, 460 patients (36.8%) the Phlegm Dampness Pattern, 212 patients (16.9%) the Qi Deficiency Pattern, and 194 patients (15.5%) the Yin Deficiency Pattern. After the regression analysis, the predictive logistic equations for each pattern were determined.
Conclusion: The predictive equations for Fire Heat, Phlegm Dampness, Qi Deficiency, and Yin Deficiency would be useful to determine individual stroke patients’ pattern identification in the clinical setting. However, further studies using objective measurements are necessary to validate these data.
Methods: This study was a hospital-based multi-center trial recruited stroke patients within 30 days of symptom onset. Patients’ general information, and the variables related to diagnostic pattern identification were measured. The diagnostic pattern of each patient was identified independently by two Korean Medicine Doctors. To derive a predictive model for pattern identification, binary logistic regression analysis was applied.
Results: Among the 1,251 patients, 385 patients (30.8%) had the Fire Heat Pattern, 460 patients (36.8%) the Phlegm Dampness Pattern, 212 patients (16.9%) the Qi Deficiency Pattern, and 194 patients (15.5%) the Yin Deficiency Pattern. After the regression analysis, the predictive logistic equations for each pattern were determined.
Conclusion: The predictive equations for Fire Heat, Phlegm Dampness, Qi Deficiency, and Yin Deficiency would be useful to determine individual stroke patients’ pattern identification in the clinical setting. However, further studies using objective measurements are necessary to validate these data.
목차
Introduction
Materials and Methods
Results
Discussion
References
참고문헌 (37)
참고문헌 신청
Donnan GA / 2008 / Stroke / Lancet 371(9624):1612~1623
Wardlaw JM / 2012 / Recombinant tissue plasminogen activator for acute ischaemic stroke : an updated systematic review and meta-analysis / Lancet 379(9834):2364~2372
DeMers G / 2012 / Tissue plasminogen activator and stroke : review of the literature for the clinician / J Emerg Med 43(6):1149~1154
ESPS Group / 1990 / European Stroke Prevention Study / Stroke 21(8):1122~1130
Gong S / 2017 / Effects of Intensive Blood Pressure Reduction on Acute Intracerebral Hemorrhage: A Systematic Review and Meta-analysis / Neurology. Sci Rep 7(1):10694
Wardlaw JM / 2012 / Recombinant tissue plasminogen activator for acute ischaemic stroke : an updated systematic review and meta-analysis / Lancet 379(9834):2364~2372
DeMers G / 2012 / Tissue plasminogen activator and stroke : review of the literature for the clinician / J Emerg Med 43(6):1149~1154
ESPS Group / 1990 / European Stroke Prevention Study / Stroke 21(8):1122~1130
Gong S / 2017 / Effects of Intensive Blood Pressure Reduction on Acute Intracerebral Hemorrhage: A Systematic Review and Meta-analysis / Neurology. Sci Rep 7(1):10694
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