Estimating Suicidality as an Outcome Measure in Clinical Trials of Suicide in Schizophrenia
Bibliographic record
Abstract
A Suicide Monitoring Board (SMB) evaluated putative events of suicidal behavior to determine whether they should be counted as outcome data (endpoints) in the International Suicide Prevention Trial (InterSePT) study. The InterSePT study compared clozapine and olanzapine prospectively, on reduction of suicidality in schizophrenia and schizoaffective disorder. SMB members, blinded to patient identification, evaluated packages of clinical information and the forms used to summarize the putative events and their context. The SMB members met regularly by international teleconference to discuss ratings they found problematic and to achieve consensus. Exactly 243 of the 980 patients enrolled (24.8%) were considered to have experienced 577 suicidal events. Of these events, 483 (83.7%) were confirmed by the SMB as real or valid endpoints. Before the consensus discussions, acceptable levels of agreement (K = 0.52) were reached among the SMB members regarding ratings considered as valid suicidal events; agreement increased (K = 0.64) when consensus ratings and those of on-site psychiatrists also blinded to treatment were also included. This article discusses our experience of evaluating schizophrenia patients for suicidality in general and in particular from reviewing chart information. The process of making clinical judgments regarding the seriousness of suicidal behavior in schizophrenia patients and their suicidal risk warrants further study.
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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 | 0.455 | 0.586 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".