Trajectories of suicidal ideation after first‐episode psychosis: a growth mixture modeling approach
Bibliographic record
Abstract
OBJECTIVE: The period immediately after the onset of first-episode psychosis (FEP) may present with high risk for suicidal ideation (SI) and attempts, although this risk may differ among patients. Thus, we aimed to identify trajectories of SI in a 2-years follow-up FEP cohort and to assess baseline predictors and clinical/functional evolution for each trajectory of SI. METHODS: We included 334 FEP participants with data on SI. Growth mixture modeling was used to identify trajectories of SI. Putative sociodemographic, clinical, and cognitive predictors of the distinct trajectories were examined using multinomial logistic regression. RESULTS: We identified three distinct trajectories: Non-SI trajectory (85.53% sample), Improving SI trajectory (9.58%), and Worsening SI trajectory (6.89%). Multinomial logistic regression model revealed that greater baseline pessimistic thoughts, anhedonia, and worse perceived family environment were associated with higher baseline SI followed by an Improving trajectory. Older age, longer duration of untreated psychosis, and reduced sleep predicted Worsening SI trajectory. Regarding clinical/functional evolution, individuals within the Improving SI trajectory displayed moderate depression at baseline which ameliorated during the study period, while the Worsening SI subgroup exhibited persistent mild depressive symptoms and greater functional impairment at follow-up assessments. CONCLUSION: Our findings delineated three distinct trajectories of SI among participants with FEP, one experiencing no SI, another in which SI might depend on acute depressive symptomatology, and a last subset where SI might be associated with mild but persistent clinical and functional impairments. These data provide insights for the early identification and tailored treatment of suicide in this at-risk population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".