The effect of childhood trauma on bipolar depression
Bibliographic record
Abstract
Childhood trauma (CT) is associated with an earlier onset and a more severe course of bipolar disorder (BD). However, the specific impact of CT on bipolar depression remains unclear. Herein, this study aimed to investigate the effect of CT using depressive episode frequency as a threshold for disease burden and severity. A cohort of 146 participants with BD was followed for 3 years. The effects of CT on mood episodes, hospital readmissions, suicidal ideation, and behavior were analyzed. A high number of depressive episodes were identified in participants with BD and CT exposure, with the effect being more pronounced in BD II than in BD I. A threshold of ≥4 depressive episodes serves as a sensitivity cutoff point to detect associations with severe outcomes, such as early readmission and suicidal ideation and behavior. The presence of CT increases the risk of experiencing at least one severe outcome by 80%. In our cohort, a cutoff point of ≥4 depressive episodes mediated the effect of CT on at least one severe outcome (early readmission or suicidal ideation and behavior). The study is limited by its non-probabilistic sample, recall bias, and moderate receiver operating characteristic curve value. The findings reinforce the association between CT and BD severity, highlighting the significantly higher number of depressive episodes in individuals with CT. This underscores CT as a risk factor for depressive predominant polarity and more frequent mood episodes in BD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".