Lamotrigine for acute bipolar depression: An exploratory item‐level analysis
Bibliographic record
Abstract
OBJECTIVES: Lamotrigine is used to treat bipolar depression despite inconsistent evidence. Here we present the results of an exploratory item-level analysis of pooled data from five randomized placebo-controlled trials of lamotrigine for acute bipolar depression. The goal was to determine if certain depression scale items were more responsive to lamotrigine treatment. METHODS: The pooled sample contained 1072 adult outpatients treated for up to 7-10 weeks. Depressive symptoms were measured with the Hamilton Depression Rating Scale and the Montgomery-Åsberg Depression Rating Scale. Change scores on individual scale items were compared between treatment groups. RESULTS: There were statistically significant effects on items assessing depressed mood/sadness, lack of interest/anhedonia, pessimism/guilt, and anergia/fatigue, on both scales. However, there was marked variation in the baseline symptom prevalence, and items with higher scores at baseline tended to have larger and statistically significant treatment effects. CONCLUSIONS: The results suggested a significant treatment effect on core symptoms of depression. A floor effect appeared to limit the sensitivity of other scale items. Given the exploratory nature of the analysis, firm conclusions cannot be drawn, although the results were consistent with past research. Relying on total depression scale sum scores over targeted assessments of core depressive symptoms may have impeded signal detection in the original trials.
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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 | 0.040 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".