Baseline characteristics of patients in 3 month mortality.

NIAID Data Ecosystem2026-03-13 收录

患者死亡率预测

医疗预后分析

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Baseline characteristics of patients in 3 month mortality.

创建时间：

2022-03-14

Unadjusted and adjusted odds of mortality at 30 days among all transported patients, and stratified by transport type, from multivariable regression modeling.

医疗预后分析

患者转运评估

Unadjusted and adjusted odds of mortality at 30 days among all transported patients, and stratified by transport type, from multivariable regression modeling.

Figshare2020-04-02 更新40

Predictive Accuracy of rBaux Score in predicting in-hospital mortality in Ethiopia

患者死亡率预测

临床评分模型验证

This dataset was collected in a five year (2020 - 2025) review of burn patients treated at one of the burn units in Ethioipa. It documents the severity of injury, sociodemographic factors and final ou

NIAID Data Ecosystem20

Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis

患者死亡率预测

炎症标志物

This study aimed to evaluate whether the combination of inflammatory markers could provide predictive powers for mortality in individual patients on dialysis and develop a predictive model for mortali

Figshare2018-03-02 更新10

Odds ratio for the adjusted 30-day mortality by travel time on multiple logistic regression analysis * .

医疗资源可及性

患者死亡率预测

Odds ratio for the adjusted 30-day mortality by travel time on multiple logistic regression analysis*.

NIAID Data Ecosystem40

Table_5_Machine Learning for Prediction of Outcomes in Cardiogenic Shock.DOCX

心源性休克预后

患者死亡率预测

ObjectiveThe management of cardiogenic shock (CS) in the elderly remains a major clinical challenge. Existing clinical prediction models have not performed well in assessing the prognosis of elderly p

NIAID Data Ecosystem20

Baseline characteristics of patients in 3 month mortality.

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