Clinical prescription of lithium, anticonvulsants antipsychotics, and antidepressants for major mood disorders
Bibliographic record
Abstract
BACKGROUND: As choices of treatments for bipolar disorder types I (BD1) and II (BD2) and major depressive disorder (MDD) continue to evolve, we reviewed studies directly comparing current clinical usage rates of medicinal treatments for these disorders. METHODS: Comprehensive searching of five literature databases through March 2024 identified reports on clinical drug prescription rates for BD and MDD patients. Rates were summarized and compared by random-effects meta-analyses with R-Studio software. RESULTS: A total of 18 reports (2006-2023) supported comparisons of clinically prescribed treatments for 17,572 mood-disorder patients (mean age 42.8 years; 7936 BD1 age 43.2 years; 6309 BD2, age 43.3; 3327 MDD, age 40.0). Among diagnoses: (BD1 vs. BD2 vs. MDD), treatments differed as: lithium (54.4% vs. 38.0% vs. 6.78%), second-generation antipsychotics (41.6% vs. 22.3% vs. 15.9%), valproate (25.7% vs. 21.5%; no MDD data), lamotrigine (13.1% vs. 27.2%; no MDD data), and antidepressants (34.9% vs. 46.4% vs. 77.5%). International use of lithium for BD appeared to increase between 2006 and 2023. LIMITATIONS: Outcomes were heterogeneous and requiring inclusion of lithium may introduce selection bias. CONCLUSIONS: Clinical treatment selections for BD1, BD2, and MDD patients differed substantially. Use of modern antipsychotics is undergoing major increases for both BD and MDD; optimal use of antidepressants for BD remains uncertain; and notably, international use of lithium tended to increase in the present data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".