Missing value percentage for variables. by Xin Liu (43569)

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the fullPIERS model. by Guiyou Yang (10259597)

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the PIERS-ML model. by Guiyou Yang (10259597)

AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre</p>…”

Feature engineering. by Youli Jiang (15249070)

AUC statistics comparing statistical trends in control and test populations. by Madeline Jarvis-Cross (22394247)

“…<p>To evaluate statistical trends, we calculated Kendall’s rank correlation coefficient during the pre-critical interval (here, days one to sixty), and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger trends. …”

Comparative Internal and External AUC Performance Across Single-Site Models. by Ioana Duta (18462981)

Sensitivity analysis of LMR thresholds based on ROC curves for associated variables. by Daysi Zulema Diaz-Obregón (22537744)

Effect of oral potassium supplementation on urinary potassium excretion and its diagnostic value for primary aldosteronism by Ke Zhang (115386)

“…After supplementation, 20% of patients had decreased 24 h UK, while 25%, 25%, and 40% showed increases of 0–10, 10–20, and > 20 mmol/24 h. …”

Demographic and ocular features. by Mingxi Shao (10066570)

“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H₂O₂ and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”

Machine learning model to diagnose PCG. by Mingxi Shao (10066570)

“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H₂O₂ and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”

ROC curves of TAS + SOD + MDA to diagnose PCG. by Mingxi Shao (10066570)

“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H₂O₂ and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”

Flow diagram of participants selection. by Wei Huang (36889)

“…RCS analysis revealed nonlinear positive BRI-LMM associations. Each10 units increase in BRI, ASM/ BMI decreased by 29% (β = −0.29,95% CI: −0.31, −0.28, <i><i>p</i></i> value < 0.0001). …”

Minimal data set. by Wei Huang (36889)

“…RCS analysis revealed nonlinear positive BRI-LMM associations. Each10 units increase in BRI, ASM/ BMI decreased by 29% (β = −0.29,95% CI: −0.31, −0.28, <i><i>p</i></i> value < 0.0001). …”

Weighted comparison of baseline characteristics. by Wei Huang (36889)

“…RCS analysis revealed nonlinear positive BRI-LMM associations. Each10 units increase in BRI, ASM/ BMI decreased by 29% (β = −0.29,95% CI: −0.31, −0.28, <i><i>p</i></i> value < 0.0001). …”

(A) Kaplan-Meier survival curves showing 28-day survival rates between the two groups;(B) ROC analysis of death risk factors in SFTS patients with HLH. by Bo Zhang (6559)

Clinical characteristics of IDC patients. by Gangming Zhu (21094310)

“…Besides, ADC value was increased (<i>P</i><0.001), but K^trans (<i>P</i>=0.037) and K_ep (<i>P</i>=0.004) were decreased in IDC patients with Lumina A (vs. other molecular subtypes). …”

Supplementary file 1_Analysis of the diagnostic value of peripheral blood immune inflammatory indicators of female bladder pain syndrome.zip by Yinglei Wang (3844756)

“…The correlation between these indicators and MBC is secondary outcomes. The optimal cut-off value for the parameters was identified using the receiver operating characteristic (ROC) curve and its area under the curve (AUC) over time.…”

Exclusion and enrollment summary from NHANES. by Lu Han (151620)

“…<div><p>Background</p><p>Blood-cell-based inflammatory biomarkers are increasingly recognized for their diagnostic value in infections due to their clinical accessibility. …”

Trend coefficients and AUC statistics across four control populations (“Pop’n 1” to “Pop’n 4”) and four warming populations (“Pop’n 5” to “Pop’n 8”), as calculated from empirical time series during the approach to criticality. Each statistical metric was calculated within fifteen-day sliding windows, throughout the pre-critical interval. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. Here, we show analyses of empirical time series performed within the sixty-day pre-critical interval. See analyses within the forty- and thirty-day pre-critical intervals in S7 Fig. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval. Negative values indicate a decreasing trend prior to local bifurcation, while positiv by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval. Negative values indicate a decreasing trend prior to local bifurcation, while positiv</p>…”