Differences in bipolar disorder type I and type II exposed to childhood trauma: A retrospective cohort study
Bibliographic record
Abstract
Background Childhood trauma (CT) exposure is associated with a more pernicious course in bipolar disorder (BD). However, few studies have reported differences between BD I and BD II regarding CT exposure. We explore the differences in the CT trajectories in bipolar disorders. Methods A retrospective cohort study of individuals with BD (BD I = 73 vs BD II = 73) was carried out. Early age at onset (EAO) and suicide ideation/behavior were used as severity outcomes. Timespan between EAO and treatment was documented and the associations between CT and comorbid alcohol used disorder (AUD), anxiety disorders (AD), and post-traumatic stress disorder (PTSD) were also described. Univariate, bivariate analyses, and a Poisson regression model with bootstrap resampling were used. Results Higher scores of CT, physical abuse (PA), and sexual abuse (SA) were statistically significant for BD II than BD I ( p < 0.001, p = 0.048, p < 0.001, respectively). Early age at onset, suicide ideation/behavior and treatment delay were associated with CT in both BD I and BD II. However, AUD and PTSD showed association with CT only for BD I. Limitations Sample size, non-comparison control group, and recall bias. Conclusions There are differences in CT subtype exposure between BD I and BD II with regards to early age onset, suicide ideation/behavior, delayed time to treatment, and comorbid mental disorders. These results claim for early access to strategies such as CT exposure screening in individuals with BD to detect possible pernicious course and improve the quality of life and clinical outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".