Bibliographic record
Abstract
This review introduces the treatment guidelines of acute mania by the Japanese Society of Mood Disorders (JSMD) and compares it with that of the Canadian Network for Mood and Anxiety Treatments (CANMAT). Lithium alone for mild mania and the combination of lithium and some atypical antipsychotic drugs for more severe mania are recommended by the JSMD guidelines. This recommendation is different from that of the CANMAT. As maintenance treatment after treatment of the acute phase should be considered from the start of treatment and lithium is the most recommended drug for maintenance treatment in the JSMD guideline, lithium becomes the critical drug for the treatment of mania in the JSMD guidelines. The so-called "drug lag" accounts for the difference between the two guidelines. Safer drugs for extrapyramidal symptoms and cognitive function should be preferred, because these adverse effects interfere with the functional recovery of bipolar patients. The adverse effects of hypnotics or alcohol on cognitive function should be noted carefully, because cognitive impairment influences disabilities and quality of life (QOL) in bipolar patients. New understanding of the pathophysiology of bipolar disorder, that is circadian rhythm dysfunction, may lead to its new diagnosis and treatment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".