Heterogeneous early illness courses of Korean patients with bipolar disorders: replication of the staging model
Bibliographic record
Abstract
BACKGROUND: Clinical staging of bipolar disorder (BD) requires application of real-world data, as the next step in hypothesis. This study used the staging model to analyze the long-term course of BD in Korean patients based on clinical features and treatment responses to map the progression of bipolar illness from its early phase after the onset of illness. METHODS: A total of 136 patients diagnosed with BD-I (n = 62) or BD-II (n = 74) were recruited. Their progressive stages were retrospectively evaluated. A multi-state model was used to calculate the probability of progression to each stage. Hazard ratios of covariates expected to influence different courses of BD were calculated. Using the Alda score, long-term responses to mood stabilizers depending on the current stage were compared. RESULTS: Several sub-populations showed varied courses during the first five years after the onset of illness, with 41.5% remaining in stage 2 and 53% progressing to higher stages with shortened time for transition. Profiles of patients with BD-I and BD-II were different, suggesting biologically distinct groups. Comorbid psychiatric disorders, such as obsessive-compulsive disorder (OCD) and bulimia nervosa (BN) were associated with a recurrent course (stage 3a or 3b) or a malignant course (stage 3c or 4). Early age of onset, shorter duration of illness, older age at the start of medication, and poor response to lithium affected the illness progression. CONCLUSION: We were able to apply the stage model based on episode recurrence patterns in early illness courses of Korean patients with BD. The stage progression pattern differed from the early phase in BD-I and BD-II patients. Psychotic comorbidity, age at onset, age at starting psychiatric treatment showed associations with the illness progression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".