Toward prevention of bipolar disorder in at-risk children: Potential strategies ahead of the data
Bibliographic record
Abstract
BACKGROUND: Despite the well-documented negative impact of untreated bipolar illness, approaches to early intervention in childhood-onset bipolar and related disorders are not well delineated. METHODS: We reviewed the extant treatment literature on children at high risk for bipolar disorder, with definitions based on family history, childhood adversity, and prodromal symptoms. RESULTS: A panoply of approaches have been described, but most interventions are based on an inadequate database to support their routine implementation. We classify early stage interventions as a function of their safety and tolerability with the hope that these might generate more rigorous study and a stronger database. LIMITATIONS: Critics may rightly argue that identifying viable treatment methods is premature given our lack of ability to reliably predict illness trajectory in very young children. However, many of the psychosocial and pharmacological interventions we present could have nonspecific positive effects across a variety of symptoms, syndromes, and diagnoses, further enhancing the rationale for more rigorous study. CONCLUSIONS: Early stage interventions have the potential to improve functioning in prodromal illness and exert long-term positive effects on the course of illness. Many of the safest interventions deserve consideration for implementation and dissemination studies.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".