Cardiovascular preventive care for patients with serious mental illness.
Bibliographic record
Abstract
OBJECTIVE: To determine whether patients with serious mental illness (SMI) are receiving preventive care for cardiovascular disease at the same rate as those without SMI in an interprofessional practice with a mandate to care for persons with barriers to access to the health care system. DESIGN: Quality improvement exercise using a case-matched retrospective chart review. SETTING: Somerset West Community Health Centre in downtown Ottawa, Ont. PARTICIPANTS: All patients with SMI were adult, current primary care patients from the Somerset West Community Health Centre with a recorded diagnosis of SMI (bipolar affective disorder, schizophrenia, or other psychosis) during the 2-year period from June 1, 2013, to May 31, 2015. Two control patients (current primary care patients without SMI and matched for age and sex) were randomly chosen for each patient with SMI. MAIN OUTCOME MEASURES: They had at least 1 record in their electronic chart during the 2-year study period of measurement of blood pressure, weight, body mass index, smoking status, lipid screening results, or diabetes screening results. Prevention score was calculated as the number of preventive tests documented out of the possible 6. Secondary measures included age, sex, comorbidities (diabetes, hypertension, or hyperlipidemia), mental illness diagnosis, involvement of a psychiatrist, and involvement of a mental health case worker. RESULTS: Patients with SMI had higher rates of diabetes, hypertension, and dyslipidemia. Screening rates for the 6 outcome measures were very similar between patients with and without SMI. Patients with SMI who were under the care of a psychiatrist or who had a case worker had more complete screening results than those who had neither provider. CONCLUSION: As expected, patients with SMI had higher rates of metabolic comorbidities than control patients had. Screening rates for cardiovascular risk factors were similar in the 2 groups. Involvement of mental health case workers and psychiatrists in the patients' care might be linked to more complete preventive screening.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".