Childhood factors associated with increased risk for mood episode recurrences in bipolar disorder—A systematic review
Bibliographic record
Abstract
BACKGROUND: Bipolar Disorder (BD) is a recurrent illness associated with high morbidity and mortality. The frequency of mood episode recurrence in BD is highly heterogeneous and significantly impacts the person's psychosocial functioning and well-being. Understanding the factors associated with mood recurrences could inform the prognosis and treatment. The objective of this review is to summarize the literature on factors, present during childhood, that influence recurrence. METHODOLOGY: A systematic review of PubMed (1946-2017) and PsycINFO (1884-2017) databases was conducted to identify candidate studies. Search terms included bipolar disorder, episodes, predictors, recurrences, and course. Study characteristics, risk for bias, and factors associated with recurrence were coded by two raters according to predetermined criteria. RESULTS: Twenty child studies and 28 adult studies that retrospectively evaluated childhood variables associated with mood recurrences were included. Early age of onset, low socioeconomic status, comorbid disorders, inter-episode subsyndromal mood symptoms, BD-I/II subtypes, presence of stressors, and family history of BD were associated with higher number of recurrences. LIMITATIONS: Risk factors and mood recurrences were assessed and defined in different ways, limiting generalizability. CONCLUSION: Multiple factors are associated with increased risk of mood episode recurrence in BD. Interventions targeting modifiable factors could reduce the impact of BD. For example, treatment of comorbid disorders and subsyndromal mood symptoms, coupled with appropriate cognitive behavioral and family-focused therapies could ameliorate risk related to many clinical factors. When coupled with social services to address environmental factors, the number of episodes could be reduced and the course of BD significantly improved.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".