PVS_volume changes, ΔPVS_volumes, for various radiation doses within each treatment period: 0M–3M(a), 3M–12M(b), and 12M–FollowUp(c). by Ruitian Song (11045127)

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Univariate and Multivariate analysis of risk factors for nerve injury 90-days after TKA. by Rahul H. Jayaram (21463899)

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The dataset for the paired model. by Ruitian Song (11045127)

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Patient enrollment and inclusion information. by Ruitian Song (11045127)

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Changes of PVS ratios and volumes for different treatment periods and different dose levels (HD: high dose, SD: standard dose, and RD: reduced dose). by Ruitian Song (11045127)

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The dataset for the full model analysis. by Ruitian Song (11045127)

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(A) Cardiovascular Disease and (B) type-2 Diabetes Mellitus, and (C) chronic kidney disease- related Age-Adjusted Mortality Rates per 100,000 in Adults in the United States, 1999 t... by Aman Goyal (19231397)

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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. …”

Methodological flowchart. 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. …”

Preference for the EIA vs. ETA 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. …”

Phylogenetic and domain architecture analysis of PoAce1 orthologs. by Zhongjiao Liu (11877464)

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Estimated marginal mean values representative of the average time on ball and the relative frequency of ball touches and releases per minute of match duration, performed by profess... by Emily Escreet (20552634)

Subjects: “…tailed statistical significance…”

Estimated marginal mean (± SE) age group differences in the average time on ball performed by professional soccer players on a match day for each position. by Emily Escreet (20552634)

Subjects: “…tailed statistical significance…”

Estimated marginal mean values representative of the inter-positional differences in average time on ball and the relative frequency of ball touches and releases per minute of matc... by Emily Escreet (20552634)

Subjects: “…tailed statistical significance…”

Estimated marginal mean (± SE) age group differences in the relative frequency of releases per minute, performed by professional soccer players on a match day for each position. by Emily Escreet (20552634)

Subjects: “…tailed statistical significance…”