Deep forecasting of translational impact in medical research.

The value of biomedical research-a $1.7 trillion annual investment-is ultimately determined by its downstream, real-world impact, whose predictability from simple citation metrics remains unquantified. Here we sought to determine the comparative predictability of future real-world translation-as indexed by inclusion in patents, guidelines, or policy documents-from complex models of title/abstract-level content versus citations and metadata alone. We quantify predictive performance out of sample, ahead of time, across major domains, using the entire corpus of biomedical research captured by Microsoft Academic Graph from 1990-2019, encompassing 43.3 million papers. We show that citations are only moderately predictive of translational impact. In contrast, high-dimensional models of titles, abstracts, and metadata exhibit high fidelity (area under the receiver operating curve [AUROC] > 0.9), generalize across time and domain, and transfer to recognizing papers of Nobel laureates. We argue that content-based impact models are superior to conventional, citation-based measures and sustain a stronger evidence-based claim to the objective measurement of translational potential

Deep phenotyping of patient lived experience in functional bowel disorders using machine learning

\ua9 2025. The Author(s). Contemporary clinical management relies on a diagnostic label as the primary guide to treatment. However, individual patients\u27 lived experiences vary more widely than standard diagnostic categories reflect. This is especially true for functional bowel disorders (FBDs), a heterogeneous and challenging group of syndromes where no definitive diagnostic tests, clinical biomarkers, or universally effective treatments exist. Characterising the link between disease and lived experience - in the face of marked patient heterogeneity - requires deep phenotyping of the interactions between multiple characteristics, plausibly achievable only with complex modelling approaches. In a large patient cohort (n = 1175), we developed a machine learning and Bayesian generative graph framework to better understand the lived experience of FBDs. Iterating through 59 factors available from routine clinical care, spanning patient demography, diagnosis, symptomatology, life impact, mental health indices, healthcare access requirements, COVID-19 impact, and treatment effectiveness, machine models were used to quantify the predictive fidelity of one feature from the remainder. Bayesian stochastic block models were used to delineate the network community structure underpinning the heterogeneous lived experience of FBDs. Machine models quantified patient personal health rating (R2 0.35), anxiety and depression severity (R2 0.54), employment status (balanced accuracy 96%), frequency of healthcare attendance (R2 0.71), and patient-reported treatment effectiveness variably (R2 range 0.08-0.41). Contrary to the view of many healthcare professionals, the greatest model predictors of patient-reported health and quality of life were life impact, mental well-being, employment status, and age, rather than diagnostic group or symptom severity. Patients responsive to one treatment were more likely to respond to another, leaving many others refractory to all. Clinical assessment of patients with FBDs should be less concerned with diagnostic classification than with the wider life impact of illness, including mental health and employment. The stratification of treatment response (and resistance) has implications for clinical practice and trial design, necessitating further research

Science and policy characteristics of the Paris Agreement temperature goal

<p>The Paris Agreement sets a long-term temperature goal of holding the global average temperature increase to well below 2 °C, and pursuing efforts to limit this to 1.5 °C above pre-industrial levels. Here, we present an overview of science and policy aspects related to this goal and analyse the implications for mitigation pathways. We show examples of discernible differences in impacts between 1.5 °C and 2 °C warming. At the same time, most available low emission scenarios at least temporarily exceed the 1.5 °C limit before 2100. The legacy of temperature overshoots and the feasibility of limiting warming to 1.5 °C, or below, thus become central elements of a post-Paris science agenda. The near-term mitigation targets set by countries for the 2020-2030 period are insufficient to secure the achievement of the temperature goal. An increase in mitigation ambition for this period will determine the Agreement's effectiveness in achieving its temperature goal.</p