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Christine M. Lohse

Researcher at Mayo Clinic

Publications -  579
Citations -  35309

Christine M. Lohse is an academic researcher from Mayo Clinic. The author has contributed to research in topics: Renal cell carcinoma & Nephrectomy. The author has an hindex of 87, co-authored 517 publications receiving 31313 citations. Previous affiliations of Christine M. Lohse include University of Rochester.

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An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score.

TL;DR: In patients with clear cell renal cell carcinoma 1997 TNM stage, tumor size, nuclear grade and histological tumor necrosis were significantly associated with cancer specific survival, and a scoring system based on these features can be used to predict outcome.
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Relative impact of risk factors for deep vein thrombosis and pulmonary embolism: a population-based study.

TL;DR: Estimating the population attributable risk (defined as the percentage of all cases of a disease in a population that can be "attributed" to a risk factor) for deep vein thrombosis and pulmonary embolism associated with venous thromboembolism risk factors suggests greater emphasis should be placed on prophylaxis for hospitalized medical patients.
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Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma.

TL;DR: It is demonstrated that there are significant differences in outcome and associations with outcome for the different histologic subtypes of RCC, highlighting the need for accurate subtyping.
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Solid Renal Tumors: An Analysis of Pathological Features Related to Tumor Size

TL;DR: As tumor size increased there was a significant increase in the odds of having a malignant compared to a benign tumor, clear cell compared to papillary RCC and high grade compared to low grade malignancy.

Relative Impact of Risk Factors for Deep Vein Thrombosis and Pulmonary Embolism

TL;DR: In this article, the authors developed a conditional logistic regression model appropriate to the matched case-control study design and then estimated attributable risk for the risk factors individually and collectively, together, the eight risk factors accounted for 74% of disease occurrence.