Treatment of bipolar disorder: New perspectives
Bibliographic record
Abstract
Treatment of bipolar disorder (BD) has traditionally focused on alleviation of acute symptoms and prevention of future recurrences. Current treatment guide-lines advocate more or less similar treatment algorithms for all patients. Such approach largely ignores the clinical, genetic, and pathophysiological heterogeneity of BD, which makes certain patients more (or less) likely to respond to specific treatments. Variables such as family history, comorbidity, course of illness, quality and duration of previous remissions, physical and medical comorbidity, and side-effects may help in selecting the most effective treatment for an individual patient, yet their value is not recognized by current algorithms. As well, polymorphisms of specific genes may prove useful in predicting treatment outcome and/or understanding the pharmacological mechanisms of mood stabilization. Novel molecular targets have recently emerged from studies of mechanisms of action of available mood stabilizers. They include inhibitors of protein kinase C, inhibitors of glycogen synthase kinase, or medications modulating glutamatergic neurotransmission. As well, treatment targets are moving beyond acute symptoms and prevention of mood episodes. Cognitive deficits, persistence of residual symptoms, and increased mortality of BD are recognized as important for outcome of BD, yet are not always adequately addressed by traditional treatments.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".