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Demographics for Training and Validation Sets. Numbers represent unique patients.

Figshare2025-05-12 更新2026-04-28 收录

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https://figshare.com/articles/dataset/Demographics_for_Training_and_Validation_Sets_Numbers_represent_unique_patients_/29059303 数据链接链接失效反馈

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资源简介：

Demographics for Training and Validation Sets. Numbers represent unique patients.

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创建时间：

2025-05-12

肝纤维化无创诊断

临床预测模型

Uni-variate logistic regression analysis for the presence of increased liver stiffness (≥ 9.2kPa) corresponding to significant fibrosis (≥ F2) in the validation cohort.

NIAID Data Ecosystem20

Ability of variables to predict adult patients’ subsequent requirement for Intensive Care Unit admission.

ICU重症监护室

临床预测模型

Ability of variables to predict adult patients’ subsequent requirement for Intensive Care Unit admission.

NIAID Data Ecosystem10

Supplementary file 1_Association between the C-reactive protein to albumin ratio and unplanned readmission in ulcerative colitis: insights from a cohort study.docx

溃疡性结肠炎预后

临床预测模型

ObjectiveThis study aimed to investigate the association between the C-reactive protein to albumin ratio (CAR) and unplanned readmissions in patients with ulcerative colitis (UC) and to evaluate its p

NIAID Data Ecosystem00

Bone scan positivity in non-metastatic, castrate-resistant prostate cancer: external validation study

前列腺癌骨转移预测

临床预测模型

ABSTRACT Introduction: Tables predicting the probability of a positive bone scan in men with non-metastatic, castrate-resistant prostate cancer have recently been reported. We performed an external v

NIAID Data Ecosystem20

DataSheet_1_Establishment and validation of a nomogram containing cytokeratin fragment antigen 21-1 for the differential diagnosis of intrahepatic cholangiocarcinoma and hepatocellular carcinoma.docx

肝癌研究

临床预测模型

BackgroundOur study aimed to develop a nomogram incorporating cytokeratin fragment antigen 21–1 (CYFRA21–1) to assist in differentiating between patients with intrahepatic cholangiocarcinoma (ICC) and

NIAID Data Ecosystem00

Demographics for Training and Validation Sets. Numbers represent unique patients.

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