In search of optimal lithium levels and olanzapine doses in the long-term treatment of bipolar I disorder. A post-hoc analysis of the maintenance study by Tohen et al. 2005
Bibliographic record
Abstract
PURPOSE: The aim of this study was to investigate whether lower lithium levels (LoLi) or olanzapine doses (LoOL) are risk factors for future mood episodes in patients with bipolar I disorder. METHODS: A post-hoc analysis of the olanzapine-lithium-maintenance study [31] was performed using proportional hazards Cox regression models and marginal structural models (MSMs), adjusting for non-random assignments of dose during treatment. RESULTS: The LoLi group (<0.6 mmol/L) had a significantly increased risk of manic/mixed (hazard ratio [HR]=1.96, p=0.042), but not depressive (HR=2.11, p=0.272) episodes, compared to the combined medium (0.6-0.79 mmol/L) and high lithium level (≥0.8 mmol/L) groups. There was no significant difference in risk between the two higher lithium level groups (0.6-0.79 mmol/L; ≥0.8 mmol/L) for new manic/mixed (HR=0.96, p=0.893) or depressive (HR=0.95, p=0.922) episodes. The LoOL group (<10mg/day) showed a significantly increased risk of depressive (HR=2.24, p=0.025) episodes compared to the higher olanzapine (HiOL) dose group (HiOL: 10-20 mg/day), while there was no statistically significant difference in risk for manic/mixed episodes between the two groups (HR=0.94, p=0.895). CONCLUSION: Lithium levels≥0.6 mmol/L and olanzapine doses≥10mg/day may be necessary for optimal protection against manic/mixed or depressive episodes, respectively in patients with bipolar I disorder.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".