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

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

“…As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. …”

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)

“…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>…”

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

<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. …”

Chun Fai Martin Cheng: How can we decrease metabolism in the New Zealand green-lipped mussels, Perna canaliculus, for better transport? by Cheng Martin (11717816)

The experimental set-up we used. by Hillary Jean-Joseph (9676166)

Overview of the WeARTolerance program. by Ana Beato (20489933)

“…This research evaluates the "WeARTolerance’’ arts-based program in reducing mental health stigma among diverse youths. …”

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

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

AUC ROC curve. by Dieuwke Luijten (15282736)

“…</p><p>Results</p><p>During follow-up, 28 patients had residual PH (42%). A decrease in VG-RVPO after PEA was associated with decrease in mPAP or indexed RV mass post PEA (r = 0.55, p < 0.05 and r = 0.64, p < 0.05, respectively). …”

Application of WeChat-based cognitive behavioural stress management for early-stage cervical cancer patients: a randomised controlled study by Qing Hao (566198)

“…<p>This randomised controlled study was aimed at investigating the effects of WeChat-based cognitive behavioural stress management (WB-CBSM) on the mental health of patients with early-stage cervical cancer treated with surgical resection.…”

Data Extraction Sheet: A sheet that contains the data that we extracted from the included studies. by Mohamed R. Abdelraouf (21501468)

Mitigation (decreasing) scenario. by Fabio Correddu (12501799)

Statistical analysis of the AUC measurement. by Esra Ayan (21156015)

“…<p>Analysis indicated that INSv application significantly changed spontaneous calcium activity (IAsp <i>p</i>-value: 0.1290, INSv <i>p</i>-value: 0.0002). The mean AUC decreased in both samples.…”

Screening Sheet: A sheet that contains the full screening process that we used to include or exclude any study. by Mohamed R. Abdelraouf (21501468)

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)

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)

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)

“…Negative values indicate a decreasing trend prior to local bifurcation, while positiv</p>…”

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)