The PSY-SIM Model: Using Real-World Data to Inform Health Care Policy for Individuals With Chronic Psychotic Disorders
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Few microsimulation models have been developed for chronic psychotic disorders, severe and disabling mental disorders associated with poor medical and psychiatric outcomes, and high costs of care. The objective of this work was to develop a microsimulation model for individuals with chronic psychotic disorders and to use the model to examine the impact of a smoking cessation initiative on patient outcomes. STUDY DESIGN: Using health records and survey data from Ontario, Canada, the PSY-SIM model was developed to simulate health and cost outcomes of individuals with chronic psychotic disorders. The model was then used to examine the impact of the Smoking Treatment for Ontario Patients (STOP) program from Ontario on the development of chronic conditions, life expectancy, quality of life, and lifetime health care costs. STUDY RESULTS: Individuals with chronic psychotic disorders had a lifetime risk of 63% for congestive heart failure and roughly 50% for respiratory disease, cancer and diabetes, and a life expectancy of 76 years. The model suggests the STOP program can reduce morbidity and lead to survival and quality of life gains with modest increases in health care costs. At a long-term quit rate of 4.4%, the incremental cost-effectiveness ratio of the STOP program was $41,936/QALY compared with status quo. CONCLUSIONS: Smoking cessation initiatives among individuals with chronic psychotic disorders can be cost-effective. These findings will be relevant for decision-makers and clinicians looking to improving health outcomes among this patient population.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".