The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

To evaluate the impact of a counselling programme to strengthen the health and nutrition behaviours of caregivers of children under 2 and the sustainability of that impact through reduced intervention intensity one year later.

The programme trained community- and facility-based health staff to provide nutrition counselling. We conducted an impact evaluation with a modified stepped-wedge design using difference-in-differences analysis to compare indicator changes in an intervention group to a comparison group (midterm survey) and then a full intervention group to a light intervention group (final survey).

Batken and Jalal-Abad oblasts, the Kyrgyz Republic, 2020–2023.

Caregivers of children under 2 provided 6253 responses in three telephone surveys.

We observed statistically significant differences between the intervention and comparison groups at midterm for the percentage of children consuming vitamin A-rich foods; an increase in the intervention group (58–62 %) and a decrease in the comparison group (61–57 %). We observed similar results with exclusive breastfeeding (51–55 % in the intervention group and 48–40 % in the comparison group). There were also positive differences in other health and nutrition indicators. With the final survey results, in general, we observed statistically significant differences indicating a bigger change in full intervention areas compared to light intervention areas. We observed small negative changes in many indicators in light intervention areas.

This evaluation highlights the importance of continued support for local interventions, particularly counselling programmes, to foster optimal nutrition behaviours.

There is a common perception that creativity is associated with psychopathology. Previous studies have shown that members of creative groups such as comedians, artists and scientists scores higher than the norm on psychotic traits, and scientists in STEM (science, technology, engineering and mathematics) fields score highly on autistic traits.

To test whether magicians, a creative group that has not been studied before, also score highly on psychopathological traits and autism, and to test the associations of creative self-efficacy and creative identity with schizotypal and autistic traits among magicians.

A sample of 195 magicians and 233 people from the general population completed measures of schizotypal traits (Oxford–Liverpool Inventory of Feelings and Experiences) and autism (Abridged Version of the Autism-Spectrum Quotient), as well as the Short Scale of Creative Self. Magicians were also compared with other creative groups with respect to schizotypal traits, based on previously published data.

Magicians scored lower than the general population sample on three of the four schizophrenia measures (cognitive disorganisation, introvertive anhedonia and impulsive nonconformity) but did not differ with respect to unusual experiences or autism scores. Magicians scored higher on creative self-efficacy and creative personal identity than the general sample. Magicians’ scores on schizotypal traits were largely lower than those of other creative groups. Originality of magic was positively correlated with unusual experiences (r = 0.208), creative self-efficacy (r = 0.251) and creative identity (r = 0.362).

This is the first study to show a creative group with lower scores than norms on psychotic traits. The results highlight the unique characteristics of magicians and the possible myriad associations between creativity and mental disorders among creative groups.

To meet the UK's greenhouse gas (GHG) emissions targets, the Climate Change Committee (CCC) recommended to reduce current meat and dairy intake by 20% by 2030. In this study, we modelled the impact of potential dietary changes on GHG emissions and water use with the selected scenarios based on the trend of food purchase and meat and dairy reduction policy. We show that implementing fiscal measures and facilitating innovations in production of meat alternatives would accelerate existing positive trends, help the UK reach the CCC 2030 target of 20% meat and dairy reduction and increase fruit and vegetable intake.

We used 2001–2019 data from the Family Food module of the Living Costs and Food Survey (LCF), an annual UK survey of about 5,000 representative households recording quantities of all food and drink purchases, to model four 2030 dietary scenarios: Business as usual (BAU); two fiscal policy scenarios (‘fiscal 10%’ and ‘fiscal 20%’), combining either a 10% meat and dairy tax and a 10% fruit and vegetable subsidy, or a 20% tax and 20% subsidy on the same foods; and an ‘innovation scenario’ substituting traditionally-produced meat and dairy with plant-based analogues and animal proteins produced in laboratories. Compared to 2019 levels, we forecasted reductions in the range of 5–30% for meat and 8–32% for dairy across scenarios. Meat reductions could be up to 21.5% (fiscal20%) and 30.4% (innovation). For all scenarios we forecasted an increase in fruit and vegetables intake in the range of 3–13.5%; with the fiscal 20% scenario showing highest increases (13.5%). GHG emissions and water use reductions were highest for the innovation scenario (−19.8%, −16.2%) followed by fiscal 20% (−15.8%, −9.2%) fiscal 10% (−12.1%, 5.9%) and BAU (−8.3%, −2.6%) scenarios. Compared to average households, low-income households had similar patterns of change, but both past and predicted purchase of meat, fruit and vegetables and environmental footprints were lower.

Meat and dairy-reduction policies would help meet net zero targets and improve population health in the UK.