First-generation versus second-generation antipsychotic drugs for depression in schizophrenia
Bibliographic record
Abstract
Introduction A certain degree of depressive symptoms is common in schizophrenic patients. The assessment and treatment of depressive symptoms in schizophrenia is clinically challenging. Objectives We conducted a cross-sectional study to investigate the depressive dimension of schizophrenic patients. Aims The aim was to evaluate the effect of pharmacotherapy on depressive symptomatology. Methods Thirty-four outpatients (18-65 years old) with stable schizophrenia in monotherapy with FGAs or SGAs. We evaluated: depressive symptoms with Calgary Depression Scale for Schizophrenia; positive and negative symptoms (with Positive and Negative Symptom Scale); neurocognition (with Matrics Cognitive Consensus Battery); social cognition (with Facial Emotional Identification Test); social functioning (with Personal and Social Performance Scale and with UCSD Performance-based Skills Assessment). Collected data underwent statistical analyses. Results A SGAs therapy was associated with: lower depressive symptoms (mean SGAs group = 4.0; mean FGAs group = 7.86, P < 0.05); lower mean positive symptoms (mean SGAs group = 12.65; mean FGAs group = 17.43, P < 0.05); lower negative symptoms (mean SGAs group = 21.35; mean FGAs group = 29.07, P < 0.05); lower scores on the PANSS-total (mean SGAs group = 71.05; mean FGAs group = 91.86, P < 0.01). After correction for multiple variables, the SGAs group still had significantly lower values towards the FGAs group (P < 0.05). Conclusions Our study support the notion that switch from a FGA to a SGA could be a relatively simple first-step for the treatment of this condition. Atypical antipsychotics might exercise antidepressant effects with different potential mechanism including: remission of a FGA-induced depression and action on of 5-hydroxytryptamine, dopamine [other than postsynaptic D2], and α1-noradrenergic receptor sites. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".