M82. GROWING BURDEN OF DISEASE: THE PREVALENCE OF CHRONIC HEALTH CONDITIONS AFTER A FIRST EPISODE OF PSYCHOSIS
Bibliographic record
Abstract
Abstract Background Over 12% of Canadians live with two or more (2+) co-occurring chronic physical health conditions or multimorbidity. This proportion is expected to rise with increased exposure to risk factors for these diseases. People with psychotic disorders often have co-occurring chronic physical health conditions; however, to date there has been a paucity of research on the prevalence of multimorbidity among people with psychosis. The objective of our study was to examine the prevalence of multimorbidity ten years after a first episode of psychosis (FEP) utilizing data from a retrospective cohort study based on health administrative data. Methods The health administrative dataset has been linked to data from the Prevention and Early Intervention Program for Psychoses in London, Canada to enable identification of FEP patients (n=455). FEP patients were compared to a randomly selected comparison group from the general population (n=1,783), matched on age, gender, and neighbourhood. This cohort has been followed for a 10-year period in the health administrative data to ascertain the prevalence of physical comorbidities. Results Preliminary analyses on 2,238 patients (557 females, 1,681 males) at 10-year follow-up, reveals that 32.1% (95% CI 28.0%, 36.5%) of FEP patients have 2+ conditions, as compared to 15.1% (95% CI 13.6%, 16.9%) of people without psychosis. Full results on risk factors for multimorbidity will be presented. Discussion The findings from this study will facilitate increased surveillance and recognition of the common physical health conditions faced by people with psychosis, including those contributing to premature mortality of this patient population. This information aims to assist decision-makers in creating tailored intervention plans to improve the physical health of patients with psychotic disorders, and integrate care across multiple specialties to reduce the growing burden of disease to the Canadian health system.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".