iKIR score decreases protection associated with protective class II genotypes in T1D. by Laura Mora-Bitria (20477525)

Theoretical frameworks of social protection. by Na Chen (153323)

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Mean values and 95% confidence intervals for recovery time, Average NN Interval (AVNN); Standard Deviation of NN Intervals (SDNN), Low Frequency/ High Frequency Ratio (LF/HF); and... by Robert Trybulski (10998702)

Autophagy significantly increases RGCs loss. by Hongmei Qian (538647)

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Autophagy significantly increases RGCs loss. by Hongmei Qian (538647)

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Intervention with MLKL decreased neutrophil death and protected mice against <i>E. coli</i> pneumonia. by Qinyu Luo (13000956)

Modified version of the Caregiving Stress Model for Post Intensive Care Syndrome – Family impairments. by Cristobal Padilla-Fortunatti (21376807)

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SPIRIT schedule: Timeline of recruitment and assessment. <i>t</i>₀: Daily screening; <i>t</i>₁: Between the 3^rd and 10^th day of ICU admi... by Cristobal Padilla-Fortunatti (21376807)

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Questionnaire description. by Cristobal Padilla-Fortunatti (21376807)

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Variables and generalized linear mixed models to explore PICS-F impairments. by Cristobal Padilla-Fortunatti (21376807)

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RTA408 significantly reduces RGCs loss. by Hongmei Qian (538647)

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RTA408 significantly reduces RGCs loss in glaucoma. by Hongmei Qian (538647)

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Decrease of cellular cAMP levels in response to a concentration range of melatonin treatments. by Cathryn S. D. Maienza (20629929)

Table 2 - by Rosanna Mary Rooney (17595801)

“…Additionally, it will likely highlight significant trends in risk and protective factors, such as the dual role of academic achievement—both as a risk and protective factor—and the impact of family-based interventions. …”

Preference for the EIA – conjoint results. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Marginal means – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample attribute table. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Subgroup analysis – Political affiliation. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample scenario description. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

AMCEs – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”