Bibliographic record
Abstract
Globally, a million people commit suicide every year, and 10-20 million attempt it. Mood disorders, especially major depressive disorder (MDD) and bipolar disorder, are the most common psychiatric conditions associated with suicide. Primary (psychiatric and physical illness), secondary (psychosocial), and tertiary (demographic) risk factors for suicide have been identified. Comorbid psychiatric illness, particularly anxiety symptoms or disorders, significantly increase the risk of suicidal behavior. Current standard risk assessments and precautions may be of limited value, while assessing the severity of anxiety and agitation may be more effective in identifying patients at risk. Lithium is the medication that has most consistently demonstrated an antisuicidal effect. The effects of antidepressants and conventional antipsychotics on suicide risk are uncertain, but atypical antipsychotics appear promising. Atypical antipsychotics have beneficial effects on depressed mood both in patients with MDD and in patients with bipolar disorder. In addition, data in patients with schizophrenia have demonstrated a significant improvement in the incidence of suicidal behavior with clozapine compared with olanzapine. Electroconvulsive therapy appears to have an acute benefit on suicidality.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".