Trajectories of suicidality during a 2-year early-intervention program for first-episode psychosis: A longitudinal study
Bibliographic record
Abstract
Little is known about the individual course of suicidal ideations and attempts (i.e., suicidality) after treatment initiation. We examined the trajectories of suicidality and associated risk factors over a 2-year early intervention program for first-episode psychosis in 450 patients (age range 18-35 years at admission) consecutively admitted from 2003 to 2017. Suicidality was assessed via systematic file review, while sociodemographic and clinical variables were assessed at admission. Latent class growth modelling identified three trajectories: low (69.6 %), initially high (22.9 %), and persistently high (7.6 %) suicidality. Patients who were younger, lived alone and were diagnosed with affective psychosis were significantly more likely to follow the initially high trajectory. Patients who attempted suicide up to 3 months before admission, lived alone and presented lower levels of the PANSS excited factor were significantly more likely to follow the persistently high trajectory. Attempting suicide up to 3 months before admission distinguished persistently high and initially high suicidality trajectories. Suicide risk during early intervention program for first-episode psychosis is heterogenous, with acute and enduring suicidal risk, suggesting the need to adapt suicide prevention strategies to these different risk profiles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 source (direct Gemma or distilled Codex), 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".