Current situation and influencing factors of acute treatment of bipolar disorder with mixed features in China
Bibliographic record
Abstract
Background: DSM-5 proposes the concept of bipolar disorder with “mixed features ”, which is of great benefit to clinical practice. However, the clinical management of BD with mixed features is more challenging.This investigation examined the prescribing patterns and factors influencing guidelines disconcordance for the acute treatment of bipolar disorder with mixed features in mainland China. Methods:This real-world study enrolled 688 patients with acute bipolar disorder through the National Bipolar Pathway Survey Replication (BIPAS-R). We used CUDOS-M and MINI-M scales based on DSM-5 criteria to improve the sensitivity of screening for bipolar disorder with mixed features. Guideline inconsistency judgments were determined by comparison with the Canadian Network for Mood and Anxiety Treatments(CANMAT) guidelines for treatment recommendations for bipolar disorder with mixed features. Logstic regression was used to analyze the influencing factors of guideline disconcordance. Results: Among 688 cases of acute bipolar disorder, 235 cases (34.2%) were (hypo) mania with mixed features and 213 cases (30.9%) were depression with mixed feature. Without considering the order of treatment, the inconsistency rates of (hypo) mania and depression with mixed features with the guidelines were 29.4% and 55.4%, respectively. (Hypo) mania with mixed features BD-II (OR=0.52; 95% CI 0.29-0.93), age at study entry > 24 years (OR=2.4; 95% CI 1.3-4.3), and the number of episodes > 4 in the past year in depression with mixed features (OR=1.9; 95% CI 1.08-3.6), which increased the risk of treatment disconcordance of guidelines. Conclusions:Our findings suggest that BD with mixed features is more common.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".