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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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Maaike van Gerwen MD Naomi Alpert MS Raja Flores MD Emanuela Taioli MD PhD 《American journal of industrial medicine》2020,63(2):115-120
The association between asbestos exposure, mainly in occupational settings, and malignant mesothelioma has been well established; this has prompted several countries to establish mesothelioma epidemiologic surveillance programs often at the request of national agencies. This review compares currently existing mesothelioma registries worldwide to develop a concept model for a US real-time case capture mesothelioma registry. Five countries were identified with a mesothelioma specific registry, including Italy, France, UK, Australia, and South Korea. All, except the UK, used interviews to collect exposure data. Linkage with the national death index was available or was in future plans for all registries. The registries have limited information on treatment, quality of life, and other patient-centered outcomes such as symptoms and pain management. To thoroughly collect exposure data, “real-time” enrollment is preferable; to maximize the capture of mesothelioma cases, optimal coverage, and a simplified consent process are needed. 相似文献
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Chie Teramoto PHN RN MS Satoko Nagata PhD PHN RN Reiko Okamoto PhD PHN RN Ruriko Suzuki PHN RN MS Emiko Kishi PhD PHN RN Michie Nomura DSN PHN RN Noriko Jojima PHN RN MS Masumi Nishida PhD PHN RN Keiko Koide PhD PHN RN Emiko Kusano PhD PHN RN Saori Iwamoto PhD PHN RN Sachiyo Murashima PhD PHN RN 《Public health nursing (Boston, Mass.)》2015,32(6):654-661
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Oliver Sartor MD Daniel Heinrich MD Neil Mariados MD Maria José Méndez Vidal MD Daniel Keizman MD Camilla Thellenberg Karlsson MD Avivit Peer MD Giuseppe Procopio MD Stephen J. Frank MD Kalevi Pulkkanen MD Eli Rosenbaum MD Stefano Severi MD José Trigo MD Lucia Trandafir MD Volker Wagner MD Rui Li MS Luke T. Nordquist MD 《The Prostate》2019,79(14):1683-1691
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