Prodopaminergic Drugs for Treating the Negative Symptoms of Schizophrenia
Bibliographic record
Abstract
BACKGROUND: The negative symptoms of schizophrenia pose a heavy burden on patients and relatives and represent an unmet therapeutic need. The observed association of negative symptoms with impaired reward system function has stimulated research on prodopaminergic agents as potential adjunctive treatments. METHODS: We conducted a systematic review and meta-analysis of published randomized controlled trials of amphetamine, methylphenidate, modafinil, armodafinil, lisdexamphetamine, L-dopa, levodopa, bromocriptine, cabergoline, quinagolide, lisuride, pergolide, apomorphine, ropinirole, pramipexole, piribedil, and rotigotine augmentation in schizophrenia and schizoaffective disorder.Medline, EMBASE, and several other databases as well as trial registries were searched for placebo-controlled trials. RESULTS: Ten randomized controlled trials were included in the meta-analysis, 6 trials on modafinil, 2 on armodafinil, 1 on L-dopa, and 1 on pramipexole. Overall, prodopaminergic agents did not significantly reduce negative symptoms. Restricting the analysis to studies requiring a minimum threshold for negative symptom severity, modafinil/armodafinil showed a significant but small effect on negative symptoms. A subset of studies allowed for calculating specific effects for the negative symptom dimensions diminished expression and amotivation, but no significant effect was found. Prodopaminergic agents did not increase positive symptom scores. CONCLUSIONS: The currently available evidence does not allow for formulating recommendations for the use of prodopaminergic agents for the treatment of negative symptoms. Nevertheless, the observed improvement in studies defining a minimum threshold for negative symptom severity in the absence of an increase in positive symptoms clearly supports further research on these agents.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".