Treatment Emergent Suicidal Ideation in depressed older adults
Bibliographic record
Abstract
BACKGROUND: Treatment-Emergent Suicidal Ideation (TESI) in older adults is poorly understood. We characterized TESI in older depressed adults during treatment with venlafaxine and explored whether TESI is related to antidepressant exposure versus dimensions of the psychiatric illness. We examined the relationship among medication exposure, onset of TESI, and clinical characteristics. METHODS: We analyzed data on 233 clinical trial participants with major depression and no baseline suicidal ideation who were treated for up to 12 weeks with venlafaxine XR (target dose: 150-300 mg/day). Suicidal ideation was assessed weekly with the Scale for Suicide Ideation. A Kaplan-Meier curve displayed the time course of TESI. Differences in baseline demographic and clinical variables between the TESI and Non-TESI groups were assessed with analyses of covariance or logistic regression. A final multivariate logistic regression model indicated baseline predictors of TESI. Depression treatment outcomes in subjects developing TESI versus those who did not were examined with a mixed effects model. RESULTS: TESI occurred in 10% of participants, typically with onset within 4 weeks of the start of treatment. Anxiety, and depression severity at baseline were predictors of TESI. Most TESI was mild and transient, with 6/233 participants having TESI considered clinically meaningful. TESI was not associated with venlafaxine blood levels or side effects. CONCLUSIONS: In older depressed adults, TESI is relatively uncommon and it is likely related to the underlying illness rather than to a medication adverse effect. This suggests that TESI requires continuing rather than discontinuing antidepressant treatment. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".