Images and quantitative results from bone fragment evaluation. by Marsel Ganeyev (21453399)

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Increases in temperature for the two drill systems at different positions using different drilling procedures and feed rates. The data in bold indicate the position where the highe... by Marsel Ganeyev (21453399)

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Graphs showing the mean temperature increase at the positions with the highest mean temperature increase for the MIPS (A) and MONO (B) systems and three drilling protocols (DP1, DP... by Marsel Ganeyev (21453399)

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Histologic evaluation of the osteotomy site. by Marsel Ganeyev (21453399)

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(A-C) Force versus drill depth and torque versus drill depth. by Marsel Ganeyev (21453399)

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Increases in temperature for the two drill systems at different positions using different drilling procedures. The data in bold indicate the position where the highest mean tempera... by Marsel Ganeyev (21453399)

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S1 File - by Michael Gulledge (20577135)

“…Despite the prevalence and severity of sleep disturbances during opioid withdrawal, there is a gap in our understanding of their interactions. …”

SHAP dependence plots with interaction coloring. by Wentao Yang (205781)

“…</p><p>Methods</p><p>Using National Health and Nutrition Examination Survey (NHANES) 2005–2010 data, we analyzed glucose disposal and frailty associations. …”

Screening process diagram. by Wentao Yang (205781)

“…</p><p>Methods</p><p>Using National Health and Nutrition Examination Survey (NHANES) 2005–2010 data, we analyzed glucose disposal and frailty associations. …”

SHAP waterfall plot. by Wentao Yang (205781)

“…</p><p>Methods</p><p>Using National Health and Nutrition Examination Survey (NHANES) 2005–2010 data, we analyzed glucose disposal and frailty associations. …”

SHAP decision plot. by Wentao Yang (205781)

“…</p><p>Methods</p><p>Using National Health and Nutrition Examination Survey (NHANES) 2005–2010 data, we analyzed glucose disposal and frailty associations. …”

LASSO regression visualization plot. by Wentao Yang (205781)

“…</p><p>Methods</p><p>Using National Health and Nutrition Examination Survey (NHANES) 2005–2010 data, we analyzed glucose disposal and frailty associations. …”

SHAP dependence plots. by Wentao Yang (205781)

“…</p><p>Methods</p><p>Using National Health and Nutrition Examination Survey (NHANES) 2005–2010 data, we analyzed glucose disposal and frailty associations. …”

Tertile stratified subgroup analysis. by Wentao Yang (205781)

“…</p><p>Methods</p><p>Using National Health and Nutrition Examination Survey (NHANES) 2005–2010 data, we analyzed glucose disposal and frailty associations. …”

Baseline patient characteristics. by Oscar F. C. van den Bosch (22184246)

“…While mean respiratory rate was not affected, midazolam resulted in a significant decrease in both VRR (ß = −0.071, 95% CI: −0.120 to −0.021) and VTV (ß = −0.117, 95% CI: −0.170 to −0.062). …”

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. …”