Evidence map›Paper›PMID 40100585›Full record

ReviewCurrent psychiatry reports2025

Let's Move Towards Precision Suicidology.

Philippe Courtet, P A Saiz

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current psychiatry reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Precision medicine in mental health: applications, challenges, and recommendations.European psychiatry : the journal of the Association of European Psychiatrists · 2026
    Review
  2. Review
  3. Inflammatory Biomarkers in Acute Suicidal Behaviors.International journal of molecular sciences · 2026
    Review
4 · The record

Corrections and comments

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

2 authors.

Philippe CourtetDepartment of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, IGF, University of Montpellier, CNRS, INSERM, Montpellier, 34295 Cedex 5, France. philippe.courtet@umontpellier.fr.
P A SaizDepartment of Psychiatry, Centro de Investigación Biomédica en Red, Salud Mental (CIBERSAM); Health Research Institute of the Principality of Asturias (ISPA); Institute of Neurosciences of the Principality of Asturias (INEUROPA); Health Service of the Principality of Asturias (SESPA), University of Oviedo, Oviedo, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewSuicidal behaviour remains a critical public health issue, with limited progress in reducing suicide rates despite various prevention efforts. The introduction of precision psychiatry offers hope by tailoring treatments based on individual genetic, environmental, and lifestyle factors. This approach could enhance the effectiveness of interventions, as current strategies are insufficient-many individuals who die by suicide had recently seen a doctor, but interventions often fail due to rapid progression of suicidal behaviour, reluctance to seek treatment, and poor identification of suicidal ideation. RECENT

findingsPrecision medicine, particularly through the use of machine learning and 'omics' techniques, shows promise in improving suicide prevention by identifying high-risk individuals and developing personalised interventions. Machine learning models can predict suicidal risk more accurately than traditional methods, while genetic markers and environmental factors can create comprehensive risk profiles, allowing for targeted prevention strategies. Stratification in psychiatry, especially concerning depression, is crucial, as treating depression alone does not effectively reduce suicide risk. Pharmacogenomics and emerging research on inflammation, psychological pain, and anhedonia suggest that specific treatments could be more effective for certain subgroups. Ultimately, precision medicine in suicide prevention, though challenging to implement, could revolutionise care by offering more personalised, timely, and effective interventions, potentially reducing suicide rates and improving mental health outcomes. This new approach emphasizes the importance of suicide-specific strategies and research into stratification to better target interventions based on individual patient characteristics.

Indexed as

Precision MedicineSuicide PreventionHumansMachine LearningSuicidal IdeationSuicideMachine learningPrecision medicinePreventionStratificationSuicidal behaviour

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.