Effects of antipsychotic treatment on cardio-cerebrovascular related mortality in schizophrenia: A subanalysis of a systematic review and meta-analysis with meta-regression of moderators
Bibliographic record
Abstract
To further explore the role of different antipsychotic treatments for cardio-cerebrovascular mortality, we performed several subgroup, sensitivity and meta-regression analyses based on a large previous meta-analysis focusing on cohort studies assessing mortality relative risk (RR) for cardio-cerebrovascular disorders in people with schizophrenia, comparing antipsychotic treatment versus no antipsychotic. Quality assessment through the Newcastle-Ottawa Scale (NOS) and publication bias was measured. We meta-analyzed 53 different studies (schizophrenia patients: n = 2,513,359; controls: n = 360,504,484) to highlight the differential effects of antipsychotic treatment regimens on cardio-cerebrovascular-related mortality in incident and prevalent samples of patients with schizophrenia. We found first generation antipsychotics (FGA) to be associated with higher mortality in incident samples of schizophrenia (oral FGA [RR=2.20, 95 %CI=1.29-3.77, k = 1] and any FGA [RR=1.70, 95 %CI=1.20-2.41, k = 1]). Conversely, second generation antipsychotics (SGAs) and clozapine were associated with reduced cardio-cerebrovascular-related mortality, in prevalent samples of schizophrenia. Subgroup analyses with NOS score ≥7 (higher quality) demonstrated a significantly increased cardio-cerebrovascular disorder-related mortality, among those exposed to FGAs vs SGAs. Meta-regression analyses demonstrated a larger association between antipsychotics and decreased risk of mortality with longer follow-up, recent study year, and higher number of adjustment variables. Overall, this subanalysis of a systematic review contributes to the evolving understanding of the complex role of antipsychotic treatment for cardio-cerebrovascular mortality in schizophrenia, paving the way for more targeted interventions and improved patient outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.009 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".