Bibliographic record
Abstract
Bipolar disorder is one of the most severe psychiatric illnesses, particularly when onset occurs during childhood or adolescence. With recent empirical evidence, questions regarding the existence of bipolar disorder among children and adolescents have given way to questions regarding prevalence. There are substantial risks inherent in misapplying diagnoses and treatments of bipolar disorder when not warranted and in withholding these diagnoses and treatments when they are warranted. As with adults, the course of bipolar disorder among children and adolescents diagnosed using unmodified diagnostic criteria is characterized by recovery and recurrence, functional impairment, suicidality, and high rates of comorbid psychiatric and medical problems. Discrepancies between increasing billing diagnoses and a stable epidemiologic prevalence of bipolar disorder suggest the possibility that diagnostic criteria are not being systematically applied in some clinical settings. Introducing new diagnoses may exacerbate rather than mitigate concerns regarding misdiagnosis and excessive use of mood-stabilizing medications. Several medications, particularly second-generation antipsychotics, are efficacious for treating acute manic episodes of bipolar I disorder. However, less is known regarding the treatment of other mood states and subtypes of bipolar disorder. Psychosocial treatments provide a forum in which to educate children and families regarding bipolar disorder and its treatment, and may be especially beneficial for reducing depressive symptoms. Offspring of parents with bipolar disorder are at increased risk of developing the illness, as are youth with major depressive disorder and certain psychiatric comorbidities. Preliminary findings regarding biomarkers offer hope that, in the future, these biomarkers may inform diagnostic and treatment decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".