Project description:Decision-making in the real world involves multiple abilities. The main goal of the current study was to examine the abilities underlying the Preschool Gambling task (PGT), a preschool variant of the Iowa Gambling task (IGT), in the context of an integrative decision-making framework. Preschoolers (n = 144) were given the PGT along with four novel decision-making tasks assessing either decision-making under ambiguity or decision-making under risk. Results indicated that the ability to learn from feedback, to maintain a stable preference, and to integrate losses and gains contributed to the variance in decision-making on the PGT. Furthermore, children's awareness level on the PGT contributed additional variance, suggesting both implicit and explicit processes are involved. The results partially support the integrative decision-making framework and suggest that multiple abilities contribute to individual differences in decision-making on the PGT.
Project description:Metacognition, the ability to monitor and reflect on our own mental states, enables us to assess our performance at different levels - from confidence in individual decisions to overall self-performance estimates (SPEs). It plays a particularly important part in computationally complex decisions that require a high level of cognitive resources, as the allocation of such limited resources presumably is based on metacognitive evaluations. However, little is known about metacognition in complex decisions, in particular, how people construct SPEs. Here, we examined how SPEs are modulated by task difficulty and feedback in cognitively complex economic decision-making, with reference to simple perceptual decision-making. We found that, in both types of decision-making, participants' objective performance was only affected by task difficulty but not by the presence of feedback. In complex economic decision-making, participants had lower SPEs in the absence of feedback (compared to the presence of feedback) in easy trials only but not in hard trials, while in simple perceptual decision-making, SPEs were lower in the absence of feedback in both easy and hard trials. Our findings suggest that people estimate their performance in complex economic decision-making through distinct metacognitive mechanisms for easy and hard instances.
Project description:UnlabelledA recent trial in rheumatoid arthritis found an inexpensive, but infrequently used, combination of therapies is neither inferior nor less safe than an expensive biologic drug. If the trial had been conducted over 10 years ago, arguably 100's of millions of dollars since spent on biologics could have been released to other, more effective treatments. Given the ever increasing number of trials proposed, this commentary uses the trial as an example to challenge payers and research funders to make smarter investments in clinical research to save potential future costs.Trial registrationNCT00405275 , registered 29 November 2006.
Project description:In order to influence global policy effectively, conservation scientists need to be able to provide robust predictions of the impact of alternative policies on biodiversity and measure progress towards goals using reliable indicators. We present a framework for using biodiversity indicators predictively to inform policy choices at a global level. The approach is illustrated with two case studies in which we project forwards the impacts of feasible policies on trends in biodiversity and in relevant indicators. The policies are based on targets agreed at the Convention on Biological Diversity (CBD) meeting in Nagoya in October 2010. The first case study compares protected area policies for African mammals, assessed using the Red List Index; the second example uses the Living Planet Index to assess the impact of a complete halt, versus a reduction, in bottom trawling. In the protected areas example, we find that the indicator can aid in decision-making because it is able to differentiate between the impacts of the different policies. In the bottom trawling example, the indicator exhibits some counter-intuitive behaviour, due to over-representation of some taxonomic and functional groups in the indicator, and contrasting impacts of the policies on different groups caused by trophic interactions. Our results support the need for further research on how to use predictive models and indicators to credibly track trends and inform policy. To be useful and relevant, scientists must make testable predictions about the impact of global policy on biodiversity to ensure that targets such as those set at Nagoya catalyse effective and measurable change.
Project description:Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation ("Which change in voxels would change the outcome most?"), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals ("Why does this person have AD?") with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual "fingerprints" of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data.
Project description:We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT's entry criteria, or a treatment's effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment-covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.
Project description:BackgroundThere is limited data regarding how clinicians operationalize shared decision-making (SDM) with athletes with cardiovascular diagnoses. This study was designed to explore sports cardiologists' conceptions of SDM and approaches to sports eligibility decisions.Methods20 sports cardiologists were interviewed by telephone or video conference from October 2022 to May 2023. Qualitative descriptive analysis was conducted with the transcripts.ResultsAll participants endorsed SDM for eligibility decisions, however, SDM was defined and operationalized heterogeneously. Only 6 participants specifically referenced eliciting patient preferences during SDM. Participants described variable roles for the physician in SDM and variable views on athletes' understanding, perception, and tolerance of risk. Participants thresholds for prohibitive annual risk of sudden cardiac death ranged from <1 % to >10 %.ConclusionsThese findings reinforce the general acceptance of SDM for sports eligibility decisions and highlight the need to better understand this process and identify the most effective approach for operationalization.
Project description:BackgroundThe World Health Organization (WHO) has developed the Total System Effectiveness (TSE) framework to assist national policy-makers in prioritizing vaccines. The pilot was launched in Thailand to explore the potential use of TSE in a country with established governance structures and accountable decision-making processes for immunization policy. While the existing literature informs vaccine adoption decisions in GAVI-eligible countries, this study attempts to address a gap in the literature by examining the policy process of a non-GAVI eligible country.MethodsA rotavirus vaccine (RVV) test case was used to compare the decision criteria made by the existing processes (Expanded Program on Immunization [EPI], and National List of Essential Medicines [NLEM]) for vaccine prioritization and the TSE-pilot model, using Thailand specific data.ResultsThe existing decision-making processes in Thailand and TSE were found to offer similar recommendations on the selection of a RVV product.ConclusionThe authors believe that TSE can provide a well-reasoned and step by step approach for countries, especially low- and middle-income countries (LMICs), to develop a systematic and transparent decision-making process for immunization policy.