Quality of clinical management of cardiometabolic risk factors in patients with severe mental illness in a specialist mental health care setting
Bibliographic record
Abstract
PURPOSE: Cardiometabolic disease in patients with severe mental illness is a major cause of shortened life expectancy. There is sparse evidence of real-world clinical risk prevention practice. We investigated levels of assessments of cardiometabolic risk factors and risk management interventions in patients with severe mental illness in the Norwegian mental health service according to an acknowledged international standard. METHODS: We collected data from 264 patients residing in six country-wide health trusts for: (a) assessments of cardiometabolic risk and (b) assessments of levels of risk reducing interventions. Logistic regressions were employed to investigate associations between risk and interventions. RESULTS: Complete assessments of all cardiometabolic risk variables were performed in 50% of the participants and 88% thereof had risk levels requiring intervention according to the standard. Smoking cessation advice was provided to 45% of daily smokers and 4% were referred to an intervention program. Obesity was identified in 62% and was associated with lifestyle interventions. Reassessment of psychotropic medication was done in 28% of the obese patients. Women with obesity were less likely to receive dietary advice, and use of clozapine or olanzapine reduced the chances for patients with obesity of getting weight reducing interventions. CONCLUSIONS: Nearly nine out of the ten participants were identified as being at cardiometabolic high risk and only half of the participants were adequately screened. Women with obesity and patients using antipsychotics with higher levels of cardiometabolic side effects had fewer adequate interventions. The findings underscore the need for standardized recommendations for identification and provision of cardiometabolic risk reducing interventions in all patients with severe mental illness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".