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Nice example of how research data can be validated against real world clinical data. doi.org/10.1016/j.co...
16d
doi.org
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Max Korbmacher
What can we learn from 1,000 meta-analyses across 10 different disciplines? doi.org/10.1017/rsm....
1mo
pubmed.ncbi.nlm.nih.gov/42083734/htt...
Fun facts about biological differences in sleep duration. But the title is misleading. Not about ageing but cross-sectional correlation. doi.org/10.1038/s415...
🏓😊 Dürr, P., El Gheche, M., Maeda, G.J. et al. Outplaying elite table tennis players with an autonomous robot. Nature 652, 886–891 (2026). doi.org/10.1038/s415...
29d
1mo
1mo
What can we learn from 1,000 meta-analyses across 10 different disciplines? - Volume 17 Issue 1
doi.org
What can we learn from 1,000 meta-analyses across 10 different disciplines? | Research Synthesis Methods | Cambridge Core
An autonomous robot system, Ace, combines event-based vision and reinforcement learning to compete with elite human table tennis players, highlighting the potential of physical AI agents to perform co...
Max Korbmacher
doi.org
A cross-organ, multi-omics U-shaped relationship between sleep duration and biological ageing clocks highlights the potential of sleep optimization to promote healthy ageing, lower disease risk and ex...
Outplaying elite table tennis players with an autonomous robot - Nature
Sleep chart of biological ageing clocks in middle and late life - Nature
Anatomical tumor distribution provided prognostic information beyond overall tumor burden in glioblastoma. Regional patterns expand on the anatomical basis of symptoms. The survival benefit associated...
pubmed.ncbi.nlm.nih.gov
doi.org
Spatial and Volumetric Characteristics of Glioblastoma: Associations With Clinical Presentation and Survival - PubMed
Max Korbmacher
Max Korbmacher
Max Korbmacher
If a transgender patient visits a psychiatrist in a hospital, is it fair to say that they have «severe psychiatric morbidity»? We believe research should be more rigorous. With 49 co-authors, including: @maxkorbmacher.bsky.social @erininthemorning.com @natacha.bsky.social @frende.bsky.social
15d
Anine Hartmann
Use of brain age models frequently lead to wrong conclusions - unfortunately it is not easy to fix. Fortunately, there are better alternatives. @edvardg.bsky.social @fmrib-steve.bsky.social @didacvp.bsky.social @maxwellelliott.bsky.social @k.b.walhovd www.medrxiv.org/content/10.6...
Cognitive function at age 13 predicts dementia 60 years later, even controlling for cardiovascular and cardiometabolic risk. @KWalhovd www.medrxiv.org/content/10.6...
29d
Interested in neuroimaging and cognition in aging and want to work with us? We have an open position @KWalhovd @LCBC_UiO 2411.webcruiter.no/Main2/Recrui...
1mo
www.medrxiv.org
1mo
We have a 3-year researcher position for analysing longitudinal brain MRI data in relation to cognitive- and other biomarker data.The shift to a biomarker-based definition of Alzheimer’s disease (AD) ...
2411.webcruiter.no
Brain age models - machine-learning predictions of chronological age from brain imaging - are widely interpreted as markers of accelerated brain aging. Here we show that this interpretation cannot be ...
Researcher in neuroimaging and cognitive change of aging
Reconsidering Brain Age: Why Age-Prediction Models Fail as Measures of Brain Aging
www.medrxiv.org
16d
Our letter of concern about the Finnish study is now published in Acta Paediatrica. The Finnish study has received a total of 5 letters/commentaries, so far. Most of the overlapping concerns are statistical/methodological. onlinelibrary.wiley.com/doi/10.1111/...
David R. Banos
Anders M Fjell
Anders M Fjell
Anders M Fjell
We just launched #GuesstheReplication (GutheR). It is a challenging game where you predict replicability based on the abstract and title of an original study. Play it now and beat my high score at lukasroeseler.github.io/GuessTheRepl... or use our free slides for teaching zenodo.org/records/1985...
27d
Lukas Röseler