Re‐evaluation of randomized control trials of lithium monotherapy: a cohort effect
Bibliographic record
Abstract
OBJECTIVE: The reported reduction of lithium's efficacy in the prophylaxis of bipolar illness has been attributed to various factors, including diagnostic changes and heterogeneous study designs. We attempted to quantify the impact of pre-randomization enrichment designs and diagnostic drift on randomized controlled trials (RCTs) of lithium maintenance therapy. METHODS: Using the Cochrane RCT search filter, MEDLINE, EMBASE, and PSYCHINFO were searched (1966 to June 2004) for all available randomized studies using the text word 'lithium'. Studies of 1 year minimum duration in bipolar disorder involving lithium and placebo arms were identified. Superiority trials without a placebo arm, discontinuation and mirror image studies were excluded. Standardized scales were used to assess randomization and allocation concealment. RESULTS: Nine RCTs enrolling 1432 bipolar I and II patients, randomizing 341 to lithium and 386 to placebo were identified, with 705 reported pre-randomization dropouts. The pooled odds of remaining recurrence free in two non-enriched RCTS using Research Diagnostic Criteria (RDC) or Feighner criteria were 3.2:1 (95% CI 0.65--15.46) trending in favor of lithium over placebo, and 22.0:1 (95% CI 7.0--68.7) for three trials using lithium enrichment and excluding atypical bipolar disorder. The odds of remaining recurrence free using DSM-IV criteria and lamotrigine enrichment were 1.9:1 (95% CI 1.2-2.8). CONCLUSION: Lithium maintenance RCTs differ in patient selection, design, and outcome. A cohort effect can be associated with the use of pre-randomization enrichment phases and, to a lesser extent, with diagnostic drift, compromising straightforward comparisons across three decades of lithium monotherapy in bipolar illness.
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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 | 0.473 | 0.669 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".