Cardiomyocyte apoptosis rate in the HF group. by Qingqing Hao (5464070)

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S1 Graphical abstract - by Aman Goyal (19231397)

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Surgery design. by Maria Nadinskaia (22662419)

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Morphometric analysis of histological slides in sarcopenia modeling by portal vein ligation and pyloric vein ligation. by Maria Nadinskaia (22662419)

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A comparison of Paraquat concentrations ^a in various types of plants. by Vivat Keawdounglek (9710710)

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Flowchart of included and excluded patients. by P.G.J. ter Horst (21743830)

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Prevalence of low semen quality including 95% confidence interval (CI) for medication at the ATC 3 level. by P.G.J. ter Horst (21743830)

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S1 Data - Medication, age and abstinence days associated with low semen quality: A cross-sectional study in more than 7000 men visiting the centre for reproductive medicine by P.G.J. ter Horst (21743830)

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Univariable and multivariable analyses (age-adjusted), concerning a selection of medicaments p < 0.1 and n ≥ 10. by P.G.J. ter Horst (21743830)

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Prevalence of low semen quality including 95% confidence interval (CI) for medication at the ATC 7 level. by P.G.J. ter Horst (21743830)

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Characteristics of the study group. by P.G.J. ter Horst (21743830)

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Univariable and multivariable analyses (age- and abstinence days-adjusted), concerning medicament(s) use and Low Semen Quality (LSQ). by P.G.J. ter Horst (21743830)

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A. Proportion of men with Low Semen Quality (LSQ) including 95% confidence interval (CI) versus prevalence, overall and for men on medication(s) (M); B: Point estimates with 95% CI... by P.G.J. ter Horst (21743830)

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Prevalence of low semen quality including 95% confidence interval (CI) for medication at the ATC 5 level. by P.G.J. ter Horst (21743830)

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Univariable and multivariable analyses (age- and abstinence days-adjusted), concerning a selection of medicaments with n ≥ 10 medicament users and p < 0.1 by univariate analysis.... by P.G.J. ter Horst (21743830)

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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. …”