Harm Avoidance as a Mediator of Treatment Response to Antidepressant Treatment of Patients with Major Depression
Bibliographic record
Abstract
BACKGROUND: The temperament harm avoidance (HA) has consistently demonstrated an association with major depressive disorder (MDD), serotonin functioning and reduction in depression symptoms in response to antidepressant medications targeting the serotonin system. In the current investigation, we examine HA as a potential mediator of treatment response to a serotonergic tricyclic antidepressant. METHODS: Outpatients (n = 150) with MDD were randomized to receive clomipramine or a control treatment. Patients completed the Hamilton Rating Scale for Depression (Ham-D) and the Tridimensional Personality Questionnaire (TPQ) prior to treatment initiation, and then again after at least 8 weeks of treatment. Using structural equation modeling, we evaluated a 'mediation model' in which change in HA is a mechanism of depression change in response to clomipramine, and a 'complication model' in which reduction in HA is a by-product of depression change. RESULTS: The mediation model provided a good fit to the data by all indices, whereas the complication model did not. Patients treated with clomipramine exhibited a greater decrease in HA as compared to those in the control group; moreover, HA reduction was associated with depression reduction. CONCLUSIONS: HA mediated the response to antidepressant treatment, such that any treatment effect of clomipramine occurred through HA reduction. Although replication with multiple assessment periods is required to determine if HA reduction actually precedes depression reduction, the results contribute to a growing body of literature implicating personality constructs subsumed within negative emotionality as mediators of treatment response to medications targeting serotonergic functioning.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".