Search alternatives:
significant one » significant dna (Expand Search), significant genes (Expand Search), significant among (Expand Search)
nn decrease » _ decrease (Expand Search), mean decrease (Expand Search), gy decreased (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
one based » gene based (Expand Search), home based (Expand Search)
significant one » significant dna (Expand Search), significant genes (Expand Search), significant among (Expand Search)
nn decrease » _ decrease (Expand Search), mean decrease (Expand Search), gy decreased (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
one based » gene based (Expand Search), home based (Expand Search)
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Subgroup analysis of ICU Stay based on pump type.
Published 2025“…The analysis showed that pulsatile perfusion led to a significant decrease in creatinine level [MD = −0.14, 95% CI (−0.24, −.04), P < 0.004], lactate level [MD = −8.21, 95% CI (−13.16, −3.25), P < 0.001], hospital stay [MD = −1.38, 95% CI (−2.51, −0.25), P = 0.016], ICU stay [MD = −0.47, 95% CI (−0.82, −0.13), P = 0.007], intubation time [MD = −3.73, 95% CI (−5.42, −2.04), P < 0.001], and increase in creatinine clearance [MD = 10.08, 95% CI (3.36, 16.80), P < 0.003]. …”
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Subgroup analysis of eGFR based on pump type.
Published 2025“…The analysis showed that pulsatile perfusion led to a significant decrease in creatinine level [MD = −0.14, 95% CI (−0.24, −.04), P < 0.004], lactate level [MD = −8.21, 95% CI (−13.16, −3.25), P < 0.001], hospital stay [MD = −1.38, 95% CI (−2.51, −0.25), P = 0.016], ICU stay [MD = −0.47, 95% CI (−0.82, −0.13), P = 0.007], intubation time [MD = −3.73, 95% CI (−5.42, −2.04), P < 0.001], and increase in creatinine clearance [MD = 10.08, 95% CI (3.36, 16.80), P < 0.003]. …”
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3T3-L1 cell viability after treatment with <i>M</i>. <i>paniculata</i> ethanolic extract.
Published 2024Subjects: -
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All-variable XGBoost model on the <i>significant illness</i> binary using the all-owner dataset.
Published 2024“…(b) Graph depicting the relative contribution of predictor variables to the all-variable XGBoost model for the <i>significant illness</i> binary. ‘Importance’ represents fractional contribution of each feature to the model, based on the total gain from including each feature; ‘cover’ represents the number of observations related to this feature in the model); ‘frequency’ represents the relative number of times a feature has been used in trees). …”
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