Treating bipolar disorder. Evidence-based guidelines for family medicine.
Bibliographic record
Abstract
OBJECTIVE: To provide an evidence-based summary of medications commonly used for bipolar disorders and a practical approach to managing bipolar disorders in the office. QUALITY OF EVIDENCE: Articles from 1990 to 2003 were selected from MEDLINE using the key words "bipolar disorder," "antiepileptics," "antipsychotics," "antidepressants," and "mood stabilizers." Good-quality evidence for many of these treatments comes from randomized trials. Lithium, divalproex, carbamazepine, lamotrigine, oxcarbazepine, and some novel antipsychotics all have level I evidence for treating various aspects of the disorder. MAIN MESSAGE: Treatment of bipolar disorder involves three therapeutic domains: acute mania, acute depression, and maintenance. Lithium has been a mainstay of treatment for some time, but antiepileptic drugs like divalproex, carbamazepine, and lamotrigine, along with novel antipsychotic drugs like olanzapine, risperidone, and quetiapine, alone or in combination, are increasingly being used successfully to treat acute mania and to maintain mood stability. CONCLUSION: Bipolar disorder is more common in family practice than previously believed. Drug treatments for this complex disorder have evolved rapidly over the past decade, radically changing its management. Treatment now tends to be very successful.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".