Treatment resistance in schizophrenia: a meta-analysis of prevalence and correlates
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence and correlates of treatment-resistant schizophrenia (TRS) through a systematic review and meta-analysis. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, an electronic search was performed in PubMed and Embase through May 17, 2022. All study designs that assessed a minimum of 20 schizophrenia-spectrum patients and provided data on TRS prevalence or allowed its calculation were included. Estimates were produced using a random-effects model meta-analysis. RESULTS: The TRS prevalence across 50 studies (n = 29,390) was 36.7% (95%CI 33.1-40.5, p < 0.0001). The prevalence ranged from 22% (95%CI 18.4-25.8) in first-episode to 39.5% (95%CI 32.2-47.0) in multiple-episode samples (Q = 18.27, p < 0.0001). Primary treatment resistance, defined as no response from the first episode, was 23.6% (95%CI 20.5-26.8) vs. 9.3% (95%CI 6.8-12.2) for later-onset/secondary (≥ 6 months after initial treatment response). Longer illness duration and recruitment from long-term hospitals or clozapine clinics were associated with higher prevalence estimates. In meta-regression analyses, older age and poor functioning predicted greater TRS. When including only studies with lower bias risk, the TRS prevalence was 28.4%. CONCLUSION: Different study designs and recruitment strategies accounted for most of the observed heterogeneity in TRS prevalence rates. The results point to early-onset and later-onset TRS as two separate disease pathways requiring clinical attention.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it