Prevalence, clinical presentation and differential diagnosis of pediatric bipolar disorder.
Bibliographic record
Abstract
BACKGROUND: Over the past 20 years, the evidence regarding pediatric bipolar disorder (BP) has increased substantially. As a result, recent concerns have focused primarily on prevalence and differential diagnosis. METHOD: Selective review of the literature. RESULTS: BP as defined by rigorously applying diagnostic criteria has been observed among children and especially adolescents in numerous countries. In contrast to increasing diagnoses in clinical settings, prevalence in epidemiologic studies has not recently changed. BPspectrum conditions among youth are highly impairing and confer high risk for conversion to BP-I and BP-II. Compared to adults, youth with BP have more mixed symptoms, more changes in mood polarity, are more often symptomatic and seem to have worse prognosis. The course, clinical characteristics, and comorbidities of BP among children and adolescents are in many ways otherwise similar to those of adults with BP. Nonetheless, many youth with BP receive no treatment and most do not receive BP-specific treatment. CONCLUSION: Despite increased evidence supporting the validity of pediatric BP, discrepancies between clinical and epidemiologic findings suggest that diagnostic misapplication may be common. Simultaneously, low rates of treatment of youth with BP suggest that withholding of BP diagnoses may also be common. Clinicians should apply diagnostic criteria rigorously in order to optimize diagnostic accuracy and ensure appropriate treatment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".