Reflexive prediction in (epi)genetics:
Collaboration with clinical geneticist Angus Clark. Angus works both as a professor in the Cardiff University's Division of Cancer & Genetics (50%) and as (honorary) consultant in the All Wales Medical Genomics Service (50%). Angus' empirical observations of the ways in which genetic predictions about health impacted health outcomes matched the ways in which Mayli described (potential) reflexive predictions in genetics and medicine, more broadly. A first article is describing the more straightforward reflexivity between predictions of health and health outcomes through the conscious and/or explicit responses to the predictions. A second article will go deeper into the not so obvious, more covert, psychological and/or subconscious responses that influence health outcomes.
Reflexive prediction in medicine, more broadly:
Mayli's comprehensive theory for analysis of all self-fulfilling prophecies allows anyone to examine the 4 conditions required for any reflexivity and for self-fulfilling reflexivity in particular. Central to each investigation is the identification and examination of the "employment sensitivity" in any particular case. Understanding employment sensitivity in a given case opens up the potential for controlling and shaping any kind of reflexivity. A first publication describes the full fledged theory of self-fulfilling prophecy in medicine and biology. A second article will mirror this theory and analysis from the self-defeating side of the reflexive spectrum. A final article compares the two and brings the full spectrum into vision.
The rise of AI has brought the use of automated prediction in clinical research and practice to the next level. Yet, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. With a research team from Duke-NUS Medical School in Singapore, specialised in Resuscitation science, Mayli wrote an overview that aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research.
Predictions are typically made in response to uncertainty, in order to inform the course of action. However, there are two kinds of uncertainty in medicine that are often confounded into a general medical uncertainty. In order to facilitate analysis of reflexive predictions, Mayli offers conceptual clarity between physiological and normative uncertainty. Whereas the former invites predictions about the feasibility of treatment to reach a specific treatment goal, the latter invites predictions about whether a specific treatment goal is desirable. The practical relevance and implementation of this conceptual work is showcased in the research collaboration on fertility outcome measures.
Collaboration on fertility outcome measures with Heidi Mertes, associate professor in Medical Ethics at Ghent University and one of the founding members of both the METAMEDICA consortium and of the Bioethics Institute Ghent. Heidi's empirical findings from her research on the ethics of medically assisted reproduction, embryo research, genetic parenthood, fertility education, and disruptive innovation in healthcare matched Mayli's reflexive theory of how conceptual predictions can be self-fulfilling in both normative and physiological ways. Their article first describes precisely in what ways fertility outcome measure become interpretative self-fulfilling prophecies: those who achieve the goal consider themselves successful and those who do not consider themselves failures. Second, it offers an alternative fertility outcome measure which can be equally self-fulfilling, according to which a successful treatment is one in which people leave the clinic released from the suffering that accompanied their status as infertile when they first entered the clinic. This new outcome measure still implies that walking out with a healthy baby is a positive outcome. What changes is that walking out without a baby can also be a positive outcome, rather than being marked exclusively as a failure.
