ArticleBMJ mental health2026
Automatically detecting trends and open questions from mental health publications: a Wellcome-funded GALENOS project.
Article in BMJ mental health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- The GALENOS approach to triangulating evidence: a structured approach for integrating information from human and animal studies.BMC medical research methodology · 2026Article
- Automatically detecting trends and open questions from mental health publications: a Wellcome-funded GALENOS project.BMJ mental health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundMore effective and better tolerated treatments are urgently needed for people with mental health disorders, such as anxiety, depression and psychosis. However, the rate of translation of positive results from early phase studies into clinically validated treatments remains painstakingly slow. The scientific literature on mental health preclinical and early interventions is burgeoning at pace, making it difficult for researchers, practitioners and policymakers to identify and track new developments.
objectiveAs part of the Wellcome-funded Global Alliance of Living Evidence for aNxiety, depressiOn and pSychosis project, we aimed to develop and evaluate an automated approach to track the evolution of mental health research over time, detect emerging trends and suggest open questions.
methodsOur approach used topic modelling, large language models and time-series forecasting in combination. We applied our approach to a corpus of 182 747 titles and abstracts extracted from the OpenAlex database for 2015-2025. Using topic modelling to identify topics and then tracking topic mentions over time, we built a time series predictive model and predicted 'trendiness' based on sustained increased mentions above baseline expected from model predictions. We evaluated our approach retrospectively using a blinded expert study of a randomly selected sample of trending and not trending topics. Finally, we developed a novel topic-augmented generation approach to suggest open questions in trendy topics and evaluated the approach by comparison to baseline-generated questions without topic augmentation.
findingsOur approach detected 973 topics and predicted 165 (17%) of those as trending. Key topics that the model predicted as trending included 'ketamine for treatment-resistant depression', 'student mental health in academia' and 'COVID-19 psychosis'. We found that domain experts largely agreed with the model's predictions of trendiness. Topic-augmented generated questions were more specific than baseline generated questions.
conclusionsOur approach enables identification of new developments and open questions. Future work will improve temporal pattern tracking and use full texts. CLINICAL IMPLICATIONS: Our approach can support all stakeholders to gain an overview of the published literature, assess temporal patterns, identify trends and rank open questions.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.