Bibliographic record
Abstract
Bipolar disorder often has long delays to first diagnosis and treatment. Both early onset and treatment delay are risk factors for a poor outcome in adulthood. Poor recognition and treatment of the illness can lead to an accumulation of episodes with their attendant risks for cycle acceleration, neurobiological abnormalities, treatment resistance, cognitive dysfunction, and premature loss of many years of life expectancy. Complicating the appropriate diagnosis is the highly variable presentation of the illness and its multiple imitators and comorbidities, including anxiety disorders, attention-deficit/hyperactivity disorder, oppositional defiant disorder, depression, and substance abuse. One of the most critical keys to correct diagnosis is the longitudinal perspective, both retrospectively assessed in detail and systematically continued prospectively. Awareness of the high incidence of childhood-onset bipolar disorder in the United States compared with Canada and most European countries will hopefully lead to correction of one of the remedial risk factors for a poor outcome—the duration of delay to first treatment. With early and sustained treatment of a first episode of mania, episode recurrence and its attendant cognitive dysfunction may be prevented. Episodes, stressors, and bouts of substance abuse can accumulate and sensitize to further and more severe occurrences, likely on an epigenetic basis. Early diagnosis and treatment are imperative to stopping these mechanisms of illness progression in bipolar disorder.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.027 |
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".