The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)

TITAN decreaser diatom heatmap. by Brent J. Bellinger (21156150)

ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.…”

TITAN decreaser macroinvertebrate heatmap Heatmap of sensitive (i.e., <i>z-</i>) macroinvertebrate taxa (y-axis) to at least one of the five stressors, in decreasing order of numbe... by Brent J. Bellinger (21156150)

Explanation of condition nodes and decision nodes in the SCM, ordered by decreasing centrality. by Stella D. Juventia (19961247)

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…The locomotion values (traces and metrics) are in arbitrary units with larger integers representing a greater displacement of the spherical treadmill, the hemodynamic (Hbt) values (traces and metrics) are a percentage change from the normalised baseline (prior to stimulus presentation), and the corresponding time series vector is presented in seconds. …”

S2 File - Factors influencing effective decrease of controlled attenuation parameters in metabolic-associated steatotic liver disease: A multilevel linear regression analysis at Va... by Sonsawan Sangprasert (22772538)

TITAN increaser macroinvertebrate heatmap Heatmap of tolerant (i.e., <i>z</i>+) macroinvertebrate taxa (y-axis) to at least one of the five stressors, in decreasing order of number... by Brent J. Bellinger (21156150)

Area under the curve (AUC) for external validation performance. by Junichi Kushioka (12236447)

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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)

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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)

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Changes in blood pressure, HbA1c, blood lipid and liver function-related indicator values compared with baseline values in all patients over time. by Jia Song (495396)

AUC of various models to predict imeglimin efficacy for liver function evaluated by ROC curves. by Naruyoshi Gyoutoku (22537831)

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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>…”

Column charts of WiAUC in therapy and control group at baseline and follow-up. by Felix L. Herr (21544362)

Input parameter ranking for discrete measurements: Each column displays parameters (P) and their influence values (E) calculated using Eq 11, shown in decreasing order of influence.... by Harry Saxton (19165441)

“…<p>Input parameter ranking for discrete measurements: Each column displays parameters (P) and their influence values (E) calculated using <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0326112#pone.0326112.e114" target="_blank">Eq 11</a>, shown in decreasing order of influence. …”

Calculation of peak force and impulse values from force plate data. by Leena Inkilä (20614460)

Area under the ROC Curve (AUC) analysis to predict AIS conversion at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

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Area under the ROC curve (AUC) analysis to predict neurological severity at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

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Change of average Hounsfield units in target lesion for patients treated with combined hyperthermia-radiotherapy (A) and radiotherapy alone by Young Kyu Lee (14076058)

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