Improving Metabolic and Cardiovascular Health at an Early Psychosis Intervention Program in Vancouver, Canada
Bibliographic record
Abstract
Psychotic disorders most commonly appear during the late teenage years and early adulthood. A focused and rapid clinical response by an integrated health team can help to improve the quality of life of the patient, leading to a better long-term prognosis. The Vancouver Coastal Health early psychosis intervention program covers a catchment area of approximately 800,000 people in the cities of Vancouver and Richmond, Canada. The program provides a multidisciplinary approach to supporting patients under the age of 30 who have recently experienced first-break psychosis. The program addresses the needs of the treatment environment, medication, and psychological therapies. A critical part of this support includes a program to specifically improve patients' physical health. Physical health needs are addressed through a two-pronged, parallel approach. Patients receive routine metabolic health assessments during their first year in the program, where standard metabolic parameters are recorded. Based on the results of clinical interviews and laboratory tests, specific actionable interventions are recommended. The second key strategy is a program that promotes healthy lifestyle goal development. Patients work closely with occupational therapists to develop goals to improve cardiometabolic health. These programs are supported by an active research environment, where patients are able to engage in studies with a focus on improving their physical health. These studies include a longitudinal evaluation of the effects of integrated health coaching on maintaining cardiometabolic health in patients recently admitted to the program, as well as a clinical study that evaluates the effects of low versus higher metabolic risk antipsychotic drugs on central adiposity. An additional pharmacogenomic study is helping to identify genetic variants that may predict cardiometabolic changes following treatment with antipsychotic drugs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".