Quality of life in patients with bipolar I depression: data from 920 patients
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of acute depression on quality of life (QOL) in patients with bipolar I disorder and to compare these results with published data on QOL in patients with unipolar depression. METHODS: Quality of life was assessed using the SF-36 in bipolar patients (n = 958) who had recently experienced an episode of acute bipolar depression and participated in a large randomized, double-blind, safety and efficacy trial. Seven studies that included SF-36 data from patients with unipolar depression were identified in the published literature and descriptive comparisons of SF-36 scores were made between the unipolar depression trials and this bipolar depression trial. RESULTS: There were 920 patients who completed the SF-36. Mean transformed scores, which could range from 0 to 100, were very low in bipolar depressed patients for the role-physical (36.7), vitality (22.4), social functioning (29.9), role-emotion (11.4), and mental health (31.0) subscales. Mean SF-36 scores for all subscales were significantly and inversely correlated (p < 0.0001) with the HAM-D indicating that patients with milder depressive symptoms had better QOL. Further, the mean SF-36 scores for the bipolar sample were consistently lower compared with published data on QOL in unipolar depression on four of the eight subscales: general health; social functioning; role-physical, and role-emotional. CONCLUSIONS: While both unipolar and bipolar depression have serious detrimental effects on patient QOL, our results suggest that some aspects of QOL may be worse in bipolar depression.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".