Childhood Abuse History in Depression Predicts Better Response to Antidepressants with Higher Serotonin Transporter Affinity: A Pilot Investigation
Bibliographic record
Abstract
OBJECTIVES: Childhood abuse is a powerful prognostic indicator in adults with major depressive disorder (MDD) and is associated with numerous biological risk factors for depression. The purpose of this investigation was to explore if antidepressant medication affinity for the serotonin transporter moderates the association between childhood abuse and treatment response. METHODS: Our sample included 52 outpatients with MDD who had received up to 26 weeks of pharmacotherapy, stratifying antidepressant medications with a high versus a low affinity for the serotonin transporter. Patients completed the Hamilton Rating Scale for Depression, Beck Depression Inventory II, Home Environment Questionnaire, and Ontario Health Supplement: Child Abuse and Trauma Scale to assess depression and childhood abuse. RESULTS: Medication class moderated the link between 3 indices of childhood abuse and treatment response such that higher levels of childhood abuse were associated with higher levels of depression severity after treatment only in those patients receiving antidepressant medications with a weak affinity for the serotonin transporter. CONCLUSIONS: This pilot study suggested that prolonged exposure to stress during childhood may result in biological vulnerabilities for depression, which may in turn be differentially targeted by pharmacological agents which target serotonin to a greater or lesser degree.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".