The Diagnosis and Treatment of Comorbid Bipolar and Substance Use Disorders
Bibliographic record
Abstract
Les troubles bipolaires et les troubles de l’utilisation fréquente de substances se produisent de façon concomitante, compliquant le diagnostic et le traitement, ce qui entraîne souvent des résultats plus graves pour les deux troubles. Un cas clinique est utilisé pour illustrer des moyens pour différencier le trouble bipolaire des symptômes induits par des substances, puis examiner les options de traitement fondées sur des données probantes pour les troubles bipolaires simultanés et les troubles liés à la consommation de substances. ABSTRACT Bipolar disorders and substance use disorders frequently co-occur complicating diagnosis and treatment, often resulting in worse outcomes for both disorders. A clinical case is used to illustrate means to differentiate bipolar disorder from substance-induced symptoms, then review evidence-based treatment options for comorbid bipolar and substance use disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".