Observational studyBMC psychiatry2026
Identifying past-year self-reported suicidality in outpatients with somatic symptom disorder using an interpretable machine-learning model: a multicenter study with an online calculator.
Observational study in BMC psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundSomatic symptom disorder (SSD) is associated with an elevated risk of suicidality. However, clinically implementable tools to identify outpatients with SSD who may warrant prioritized suicidality assessment remain limited. We therefore aimed to develop an interpretable model using routinely available outpatient data to stratify the likelihood of past-year self-reported suicidality.
methodsWe analyzed a multicenter cross-sectional registry from 3 hospitals in Ganzhou including adults aged 18-60 years with DSM-5-defined SSD. Past-year self-reported suicidality was assessed using a prespecified binary (yes/no) item. Data were split 70/30 into training/test sets. Candidate predictors were selected by the intersection of least absolute shrinkage and selection operator and Boruta. Eight algorithms were trained with repeated 5-fold cross-validation and compared primarily by area under the receiver operating characteristic curve (AUC) and Brier score; the top model underwent calibration and decision-curve analysis. Shapley additive explanations (SHAP) provided model explanations; a Shiny web calculator was implemented.
resultsOf 899 participants (median age, 33 years; 64.4% female), 19.9% reported past-year suicidality. All models showed high discrimination in the test set (AUCs > 0.900). The random forest (RANGER implementation) performed best (AUC, 0.978; 95% CI, 0.955-1.000; area under the precision-recall curve, 0.960; Brier, 0.028; accuracy, 0.967; sensitivity, 0.927; specificity, 0.977), with good calibration and favorable net clinical benefit on DCA. SHAP ranked insomnia severity index as the leading contributor, followed by the five facet mindfulness questionnaire and the repeatable battery for the assessment of neuropsychological status.
conclusionsIn SSD outpatients, an interpretable RANGER-based model showed strong internal performance for classifying participants who reported past-year self-reported suicidality, and yielded favorable clinical net benefit across relevant decision thresholds. A web-based calculator illustrates potential usability in outpatient settings; external validation and prospective implementation studies are warranted before routine adoption.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.