S86. THE RISK OF PHYSICAL HEALTH MULTIMORBIDITY IN PATIENTS WITH PSYCHOSIS: SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Patients with psychotic disorders have a reduced life expectancy compared to those without psychosis, and this is primarily due to differences in physical illnesses. We aimed to assess the risk of developing multimorbidity or multiple chronic physical health conditions, and quantify its prevalence among this clinical population. Methods We identified studies through MEDLINE, EMBASE and PsycINFO from 1990 to September 2019, and reference scanning of included studies. Quantitative studies were included if proportions of 2+ and/or 3+ chronic physical health conditions were evaluated for patients with psychosis. Findings from all studies were descriptively summarized, and estimates for risk and prevalence of multimorbidity were meta-analyzed using random effects models. Results Fourteen studies were included, eight of which were meta-analyzed to determine the risk of developing 2+ chronic conditions for patients with psychosis. The pooled meta-analytic estimate suggests that persons with psychosis have an increased risk of developing multimorbidity than those without psychotic disorders (Risk ratio, RR=1.69, 95% CI 1.37, 2.08). All studies employed different operational definitions of multimorbidity, with respect to both the number and types of included chronic conditions. Evidence from included studies suggests gender and age as potential risk factors for developing multiple chronic conditions. Discussion Patients with psychosis are 69% more likely to develop multimorbidity than those without psychotic disorders. Clinicians responsible for the management of patients with psychosis should assess their risk for somatic illnesses. Future research examining multimorbidity should employ consistent definitions to better enable cross-study comparisons.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.056 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".