Clinical, genetic, and brain imaging predictors of risk for bipolar disorder in high-risk individuals
Bibliographic record
Abstract
Introduction: Early detection and intervention in bipolar disorder (BD) might reduce illness severity, slow its progression, and, in specific cases, even ward off the full-blown disorder. Therefore, identifying at-risk individuals and targeting them promptly before the illness onset is of the utmost importance. In the last decades, there has been a significant effort aimed at identifying genetic and molecular factors able to modulate risk and pharmacological outcomes.Areas covered: We performed a narrative review of articles aimed at identifying clinical, genetics, molecular, and brain imaging markers of BD specifically focusing on samples of individuals at high-risk for BD. Special emphasis was put on studies applying an integrative design, e.g. studies combining different markers such as genetic and brain imaging.Expert opinion: Findings from studies in risk individuals are still too sparse to allow drawing definite conclusions. However, the high potentiality of longitudinal studies in individuals considered at risk to develop BD supports the need for more efforts. Future investigations should focus on more homogeneous subpopulations and evaluate the cross-linking between clinical, genetic, and brain morphostructural/functional neuroimaging characteristics as predictors of risk for BD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".