Interventions to Improve Metabolic Risk Screening Among Children and Adolescents on Antipsychotic Medication: A Systematic Review
Bibliographic record
Abstract
Objective: Antipsychotic use among youth is common and is associated with metabolic side effects such as weight gain. Guidelines recommend periodic screening of metabolic measures in youth prescribed antipsychotics; however, a guideline-to-practice gap exists. We systematically reviewed the literature to synthesize the knowledge from interventions that aim to improve antipsychotic metabolic screening. We described the interventions' effect on screening rates, the strategies used for improvement, and study quality. Methods: We conducted a systematic review of studies that attempted to improve antipsychotic metabolic risk screening practices among pediatric populations published between 2004 and August 2019. We included studies with an improvement intervention that compared screening rates before and after the intervention. We extracted data about study characteristics, screening rates in pre- and postintervention groups, strategies used to influence screening practices, and assessed studies' risk of bias. This review was prospectively registered with PROSPERO #CRD42018088241. Results: We identified six studies that demonstrated modest improvements in median metabolic screening rates for waist circumference (0%–16%), glucose (9%–39%), and lipids (11%–37%). Median postintervention screening rates were higher for weight and blood pressure (84% and 72.5%) compared with glucose and lipids (39% and 37%). Interventions used a variety of improvement strategies to address patient-, provider-, and organization-level barriers for screening, including increasing patient and provider knowledge regarding antipsychotic side effects, fostering social clinical environments that promote screening, and organizational commitment for screening antipsychotic-treated youth. All interventions were deemed at high risk of bias due to uncontrolled design and lack of adjustment for confounders. Conclusions: Included studies reported partial success in improving antipsychotic screening rates but were of poor methodological quality. Common improvement strategies may affect provider behavior to conduct metabolic screening, but these need to be tailored to local resources and organization structure. Future studies need to use rigorous methodology and theory-informed improvement strategies aligned with organizational actions to prioritize safe and judicious practice of antipsychotics among pediatric populations.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| 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".