The developmental trajectory of bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Bipolar disorder is highly heritable and therefore longitudinal observation of children of affected parents is important to mapping the early natural history. AIMS: To model the developmental trajectory of bipolar disorder based on the latest findings from an ongoing prospective study of the offspring of parents with well-characterised bipolar disorder. METHOD: A total of 229 offspring from families in which 1 parent had confirmed bipolar disorder and 86 control offspring were prospectively studied for up to 16 years. High-risk offspring were divided into subgroups based on the parental long-term response to lithium. Offspring were clinically assessed and DSM-IV diagnoses determined on masked consensus review using best estimate procedure. Adjusted survival analysis and generalised estimating equations were used to calculate differences in lifetime psychopathology. Multistate models were used to examine the progression through proposed clinical stages. RESULTS: High-risk offspring had an increased lifetime risk of a broad spectrum of disorders including bipolar disorder (hazard ratio (HR) = 20.89; P = 0.04), major depressive disorder (HR = 17.16; P = 0.004), anxiety (HR = 2.20; P = 0.03), sleep (HR = 28.21; P = 0.02) and substance use disorders (HR = 2.60; P = 0.05) compared with controls. However, only offspring from lithium non-responsive parents developed psychotic disorders. Childhood anxiety disorder predicted an increased risk of major mood disorder and evidence supported a progressive transition through clinical stages, from non-specific psychopathology to depressive and then manic or psychotic episodes. CONCLUSIONS: Findings underscore the importance of a developmental approach in conjunction with an appreciation of familial risk to facilitate earlier accurate diagnosis in symptomatic youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".