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 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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".