Transitioning to bipolar disorder: A systematic review of prospective high-risk studies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Bipolar disorder is a highly heritable condition, which can progress from an asymptomatic period in at-risk individuals to a potentially debilitating illness. Identifying individuals who are at a high risk of developing bipolar disorder may provide an opportunity for early intervention to improve outcomes. The main objective of this systematic review is to provide an overview of prospective studies that evaluated the incidence and predictors of transitioning to bipolar disorder among high-risk individuals. RECENT FINDINGS: Twenty-three publications from 16 cohorts were included in the final review. Most studies focused on familial high-risk groups, while others either used clinical or a combination of clinical and genetic risk factors. The follow-up length was from 1 to 21 years and the rate of conversion to bipolar disorder was between 8 and 25% among different studies. Overall, the results suggest that a combination of genetic and clinical risk factors; namely, subthreshold (hypo)manic symptoms and elevated depressive symptoms, may be required to optimally predict conversion to bipolar disorder. SUMMARY: The concept of high-risk for bipolar disorder is still in its infancy. Further discussions are needed to work towards an expert consensus on the high-risk criteria for bipolar disorder, taking into account both clinical and genetic risk factors.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".