Bibliographic record
Abstract
Lithium and valproate are well recognized as mood-stabilizing medications. However, a significant number of patients with bipolar disorder do not respond to or cannot tolerate the side effects of these drugs. As a result, a search for safer and more effective mood stabilizers for the treatment of bipolar disorder is ongoing. Antipsychotic medications have long been used as adjunctive therapy in combination with mood-stabilizing medications. Although conventional neuroleptics (also known as typical antipsychotics) such as haloperidol or chlorpromazine are effective antimanic agents, they do not appear to have any efficacy in treating comorbid depressive symptoms. Furthermore, typical antipsychotics are associated with a number of well-known side effects, such as extrapyramidal symptoms and tardive dyskinesia. Mood-stabilizing effects have recently been reported for a number of newer "atypical" antipsychotics that have a broader spectrum of efficacy and better safety profiles than the typical antipsychotics. The results of several clinical trials suggest that atypical antipsychotics, including risperidone, olanzapine, ziprasidone, and quetiapine, are effective for the treatment of acute mania, and open-label studies suggest that atypical antipsychotics may have long-term mood-stabilizing effects.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".