Article in eLife, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
9 authors.
Zhihao Wang *Center for Neurocognition and Social Behavior, Institute of Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, China.ORCID https://orcid.org/0000-0001-6292-9307
Tian Nan *Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0009-0009-1261-1902
Fengmei LuThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Yu YueThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Xiao CaiThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Zongling HeThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Yuejia LuoBeijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China.
Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project 2021ZD0200600Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project 2021ZD0200601Guangdong Provincial Advanced Education Institutions Young Innovative Talent Project 2025WQNCX013Major Project of National Social Science Foundation 20&ZD153Ministry of Education Humanities and Social Sciences 25YJC190023National Key R&D Program of China 2024YFE0215100National Natural Science Foundation of China 31920103009National Natural Science Foundation of China 32500929National Natural Science Foundation of China 62173069Shenzhen-Hong Kong Institute of Brain Science - Shenzhen Fundamental Research Institutions 2019SHIBS0003The General Program of Natural Science Foundation of Sichuan Province 2025JDKP0102The General Program of Natural Science Foundation of Sichuan Province 2026NSFSC0468The Science and Technology Bureau of Chengdu Program 2022-YF09-00023-SNThe Science and Technology Bureau of Chengdu Program 2024-YF05-00873-SN
6 · The paper itself
Abstract
Suicidal thoughts and behaviors (STB) are among the leading causes of death worldwide. Although previous research has consistently documented elevated risk-taking in individuals with STB and identified mood disturbances as central features of suicidality, the precise cognitive and affective computational mechanisms underlying this increased risky behavior remain poorly understood. Here, 83 adolescent inpatients with affective disorders-including 58 patients with STB (S
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.
Mood computational mechanisms underlying increased risk behavior in adolescent suicidal patients. · full record | Socratic