Interventions to Improve Metabolic Risk Screening Among Adult Patients Taking Antipsychotic Medication: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Antipsychotic use is associated with elevated cardiometabolic risk. Guidelines for metabolic risk screening of individuals taking antipsychotics have been issued, but with little uptake into clinical practice. This review systematically assessed interventions that address this guideline-to-practice gap and described their quality, improvement strategies, and effect on screening rates. METHODS: Studies of interventions that addressed metabolic risk screening of adult patients taking antipsychotics, published from inception to July 2018, were selected from MEDLINE, Embase, PsycINFO, CINAHL, and Cochrane Reviews databases. Information was extracted on study characteristics; improvement strategies at the provider, patient, and system levels; and screening rates in the intervention and comparison groups. RESULTS: The review included 30 complex interventions that used between one and nine unique improvement strategies. Social influence to shift provider and health organization culture to encourage metabolic risk screening was a common strategy, as were clinical prompts and monitoring tools to capture provider attention. Most studies were deemed at high risk of bias. Relative to comparison groups, the interventions were associated with an increase in median screening rates for glucose (28% to 65%), lipids (22% to 61%), weight (19% to 67%), and blood pressure (22% to 80%). CONCLUSIONS: This knowledge synthesis points to shortcomings of current interventions to improve antipsychotic metabolic risk screening, both in quality and in outcomes. Findings may be used to inform the design of future programs. Additional interventions are needed to address the current guideline-to-practice gap, in which approximately one-third of patients are unscreened for metabolic risk.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".