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11.
Adongo Awinaba Amoah Azumah Francess Dufie Nachinaab John Onzaberigu 《Zeitschrift fur Gesundheitswissenschaften》2022,30(7):1809-1815
Journal of Public Health - The standard of health care service is very important to the development of every nation as indicated in the Millennium Sustainable Development Goals. There are already a... 相似文献
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Keith B. Diamond Ivan J. Golub Asad M. Ashraf Samuel J. Swiggett Paul V. Romeo Jack Choueka 《Seminars in Arthroplasty》2022,32(1):15-22
BackgroundWhile studies have demonstrated favorable outcomes in utilization of primary total shoulder arthroplasty (TSA) for the treatment of glenohumeral osteoarthritis (OA), adverse events such as infections can still occur. Periprosthetic joint infections (PJIs) are associated with worse outcomes and patient morbidity. The purpose of this study was to: (1) compare patient demographics amongst TSA patients with and without PJIs following primary TSA; and (2) identify patient-related risk factors for PJIs following primary TSA.MethodsPatients undergoing primary TSA for the treatment of glenohumeral OA were identified using the Mariner administrative claims database by CPT code 23,472. Laterality modifiers were utilized to ensure PJIs were developing in the correct laterality as those patients undergoing primary TSA. Inclusion for the study group consisted of patients who developed PJIs within 2-years after the index procedure, whereas patients who did not develop PJIs served as the comparison cohort. Primary outcomes analyzed included patient demographics and patient-related risk factors for PJIs following primary TSA. A stepwise backwards elimination multivariate binomial logistic regression analyses was performed to determine the odds (OR) of PJIs in patients undergoing primary TSA. A P value less than .05 was considered statistically significant.ResultsThe query yielded 15,396 patients who underwent primary TSA for glenohumeral OA, of which 191 patients developed PJIs and 15,205 did not develop PJIs. The study found statistically significant differences amongst patients who did and did not develop PJIs following primary TSA with respect to age, sex, and presence of comorbid conditions. Risk factors associated with developing PJIs following primary TSA included: pathologic weight loss (OR: 2.06, P < .0001), obesity (OR: 1.56, P = .0001), male sex (OR: 1.52, P = .007), and peripheral vascular disease (OR: 1.46, P = .022).ConclusionAs the number of primary TSAs for the treatment of glenohumeral OA increase worldwide, identifying modifiable risk-factors to reduce the incidence of infection is critical. The study found various modifiable and non-modifiable risk factors associated with developing PJIs following primary TSA. This study is valuable to orthopedists in order to identify and risk-stratify patients with regard to PJI in the setting of primary TSA for OA.Level of EvidenceLevel III; Case-Control Study 相似文献
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Journal of Prevention - 相似文献
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Carlton Corinne N. Garcia Katelyn M. Andino Mara Villalongo Ollendick Thomas H. Richey John A. 《Cognitive therapy and research》2022,46(5):916-926
Cognitive Therapy and Research - Individuals with Social Anxiety Disorder (SAD) may be at a higher risk for negative outcomes during the COVID-19 pandemic due to isolation that is both... 相似文献
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Victoria L. Parker Matthew C. Winter John A. Tidy Barry W. Hancock Julia E. Palmer Naveed Sarwar Baljeet Kaur Katie McDonald Xianne Aguiar Kamaljit Singh Nick Unsworth Imran Jabbar Allan A. Pacey Robert F. Harrison Michael J. Seckl 《International journal of cancer. Journal international du cancer》2023,152(5):986-997
Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus. 相似文献
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Dongbing Lai Emma C. Johnson Sarah Colbert Gayathri Pandey Grace Chan Lance Bauer Meredith W. Francis Victor Hesselbrock Chella Kamarajan John Kramer Weipeng Kuang Sally Kuo Samuel Kuperman Yunlong Liu Vivia McCutcheon Zhiping Pang Martin H. Plawecki Marc Schuckit Jay Tischfield Leah Wetherill Yong Zang Howard J. Edenberg Bernice Porjesz Arpana Agrawal Tatiana Foroud 《Alcoholism, clinical and experimental research》2022,46(3):374-383