The International Suicide Prevention Trial (InterSePT): Rationale and Design of a Trial Comparing the Relative Ability of Clozapine and Olanzapine To Reduce Suicidal Behavior in Schizophrenia and Schizoaffective Patients
Bibliographic record
Abstract
Suicidal behavior in patients with psychotic disorders represents a seriously undertreated life-threatening condition. The International Suicide Prevention Trial (InterSePT) is the first large-scale, prospective study designed to evaluate the potential of antipsychotic medications to reduce suicidal behaviors in patients with schizophrenia or schizoaffective disorder who are known to be at high risk for suicide. The unique challenges to study design and the solutions identified for the InterSePT study are described. These challenges included defining suicidal behavior in patients with psychosis, endpoint selection, determination of analytic strategy, and development of scales to assess suicidal behavior. Given the life-threatening nature of suicidal behavior, ethical considerations required that the design minimize suicide attempts and deaths. While the study focused primarily on treatment of suicide, opportunities were used to collect data in related areas of interest, including suicide risk factors, other efficacy measures (e.g., Positive and Negative Syndrome Scale, Covi Anxiety Scale, Calgary Depression Scale), adverse events, pharmacoeconomics, and pharmacogenetics. Because of the complexity of the design issues, a steering committee, suicide monitoring board, and publication committee were established to assist with their management.
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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 | 0.062 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".