Toward Effective Early Intervention and Prevention Strategies for Major Affective Disorders: A Review of Antecedents and Risk Factors
Bibliographic record
Abstract
OBJECTIVE: To review critically the literature pertaining to risk factors and antecedent symptoms and syndromes in order to determine an empirically based strategy for early treatment and prevention of major mood episodes. METHOD: The relevant literature is summarized, with particular emphasis on early-onset (child and adolescent) mood disorders. RESULTS: A complex interaction between biological, psychological, and sociological factors contributes to the development of a major mood disorder. Having a positive family history of mood disorder (bipolar and unipolar) and being female (unipolar) are the strongest, most reliable risk factors. There is continuity between adolescent and adult mood disorders, and subsyndromal mood disturbance in adolescents has clinical and public health significance. However, more longitudinal study is required to reliably map the course and predictive importance of mood disorders in very young children. CONCLUSIONS: Substantial evidence supports the effectiveness of early intervention and prevention efforts in children at risk for mood disorders (identified as having affected family members) and in adolescents manifesting significant mood symptoms and syndromes (especially if associated with a positive family history). However, the current level of understanding regarding the etiological significance and mechanism of risk factors associated with mood disorders does not support broad community-based primary prevention strategies in unselected populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".