The prevalence of personality disorder in schizophrenia and psychotic disorders: systematic review of rates and explanatory modelling
Bibliographic record
Abstract
BACKGROUND: Personality disorder (PD) in psychosis is poorly studied. As PD can affect outcome in mental disorders, it is important to understand its prevalence in order to plan services, understand prognosis more fully and maximize management options. MethodLiterature searching revealed 3972 potential papers. Twenty papers including 6345 patients were included in the final analysis. There was great variation in prevalence and multilevel modelling was used to identify possible reasons for this heterogeneity. RESULTS: The prevalence of PD varied from 4.5% to 100%. Multilevel analysis suggested country of study, study type, the instruments used to diagnose PD and patient care correlated with the prevalence data explaining the study level heterogeneity, with 34.2, 33.4, 17.0 and 4.5% by each variable respectively. Personality studies in Canada and Sweden reported lower PD prevalence, whereas in Spain it was higher than the multinational study. Compared with randomized controlled trials, case-control studies reported lower prevalence [odds ratio (OR)=0.35, 95% confidence interval (CI) 0.15-0.79] and observational studies higher prevalence (OR 70.5, 95% CI 8.5-583). Primary-care patients were less likely to be diagnosed (OR 0.02, 95% CI 0-0.19) than hospital patients, and out-patients had higher prevalence (OR 12.5, 95% CI 1.77-88.6). CONCLUSIONS: The reported prevalence of PD in schizophrenia varies significantly. Statistical modelling suggests care, country, study type and diagnostic tools for PD all bias prevalence rates. The number of papers reaching the inclusion criteria, the relative paucity of information and the difficulties in developing an accurate statistical model limited interpretation from the study.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".