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"(uncertainty)= \n",
"# Living Kidney Donors \n",
"\n",
"\n",
"\n",
" The distinction between static and dynamic data presentation in scientific literature is stark: nearly all peer-reviewed clinical and public health research findings rely on static visualizations of pre-determined cohorts. If personalized medicine is to fulfill its promise, dynamic visualization—such as the interactive model above—must become the standard for conveying information to patients, care providers, and public health practitioners. “Give him a mask, and he will tell you the truth.”+ Expand
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" \n",
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"Personalized Risk Estimates
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"
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"—Oscar Wilde, The Truth of Masks
\n", " The demographic shift toward older kidney donors necessitates improved characterization of risks to enhance consent processes and outcomes.\n", "\n", "Next, at the **strategic** layer, I addressed the challenge of characterizing long-term donor risk—especially relative to the elusive, counterfactual scenario of what would have happened had the donor never donated. This comparative risk framework is not just a statistical exercise; it shapes how we counsel, regulate, and frame the ethics of living donation. Risk is never absolute—it is narrated, estimated, and situated. Thus, our strategic models must be transparent about uncertainty while grounded in rigor.\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{raw} html\n", "\n", "\n", "
\n", " — Al Ammary, Muzaale, et al.\n", "