Multivariable Prediction Modeling of Antidepressant Initiation in Unipolar Depressed Adolescents: A Secondary Analysis of the IMPACT Trial
Bibliographic record
Abstract
Introduction: This secondary analysis of data collected in a randomized controlled trial (RCT) for the treatment of depression in adolescents aimed to test prediction models relating antidepressant (AD) initiation to clinical variables. Methods: The primary study was an RCT where adolescents (ages 11–17) with depression were assigned one of three outpatient psychotherapies over 86 weeks. The current study tested five registered prediction models using data on adolescents not taking ADs at baseline ( N = 337). Outcomes of interest included: AD initiation, change in depression severity, and self-injurious thoughts and behaviors (SITBs). Results: Findings from registered analytic strategies were not consistent with our a priori hypotheses; rather we unexpectedly observed a relationship between initiation of AD and increased risk of suicide attempts and suicidal ideation during the same time interval ( p > 0.01). Sensitivity analyses found that: (1) higher depressive symptom severity and self-harm each predicted future AD initiation ( p < 0.05), and (2) new-onset SITB was associated with AD initiation ( p < 0.01). Conclusions: Taken together, our results suggest that depression symptoms severity and SITBs may prompt AD initiation. Researchers may wish to further explore causal pathways relevant to the association ADs between SITBs. Clinicians need to be cognizant of high-quality guideline recommendations when prescribing ADs to adolescents.
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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 | 0.042 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".