Number Needed to Treat or Harm Analyses of Olanzapine for Maintenance Treatment of Bipolar Disorder
Bibliographic record
Abstract
The number-needed-to-treat (NNT) or the number-needed-to-harm (NNH) analysis was performed on olanzapine and comparators for all known controlled clinical studies of olanzapine for bipolar maintenance treatment or relapse prevention to assess safety and efficacy. Studies compared olanzapine (n = 225) and placebo (n = 136) for 12 months, olanzapine (n = 217) and lithium (n = 214) for 12 months, and olanzapine plus lithium or valproate (n = 72) and placebo plus lithium or valproate (n = 64) for 18 months. For prevention of all-cause treatment discontinuation, the NNT was 7 to 8. For 9 of 11 efficacy and disposition measures examined, beneficial outcomes were more common with olanzapine than placebo. Beneficial outcomes were more common with olanzapine than lithium for 7 measures and more common for olanzapine plus lithium or valproate than placebo plus lithium or valproate for 1 measure. The NNHs of 5 to 8 for a weight gain of 7% or higher and 10 to 11 for the increase in body mass index category to overweight or obese during maintenance treatment indicated that these outcomes were more common for olanzapine or olanzapine plus mood stabilizers than for the comparators. All efficacy and disposition measures showing significant differences between groups for 12 to 18 months have NNTs favoring olanzapine or olanzapine plus lithium or valproate over placebo, lithium, or placebo plus lithium or valproate. However, the NNHs favor these comparators for avoidance of weight gain and of increase in body mass index category to overweight or obese. Clinicians should consider these and other potential benefits and risks in using maintenance treatments for patients with a bipolar 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 | 0.077 | 0.118 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".