Bibliographic record
Abstract
OBJECTIVE: The primary objective of this review article is to provide a coherent, systematic synthesis of the literature on the management of suicidality in schizophrenia that is relevant to the front-line clinician. METHOD: Literature searches were conducted on MEDLINE (1996 to 2007) and PubMed (1993 to 2007), using the key words "schizophrenia" and "suicide," as well as references from the resulting articles. I used my own clinical experience to create fictional case examples to illustrate the applicability of the literature discussed in this paper. RESULTS: Suicidality in schizophrenia is high, and early detection relies on the appreciation and evaluation of the clinical manifestations of depression, despair, and hopelessness, as well as on the nature and severity of the psychotic experience itself, particularly in recent-onset patients with higher cognitive function and educational background. Clinical management includes ensuring immediate safety, the use of psychosocial techniques to address depression and psychosocial stressors, targeted pharmacotherapy for depression and psychosis, and adequate discharge planning. Clozapine is the only antipsychotic with good evidence for efficacy in decreasing suicidal behaviour in schizophrenia. CONCLUSIONS: The optimal management of suicidality in schizophrenia involves the incorporation of traditional bedside clinical skills, selection of psychosocial modalities based on individual needs, and selective pharmacotherapy directed primarily at psychotic and depressive symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".