Metabolic Monitoring Training Program Implementation in the Community Setting Was Associated with Improved Monitoring in Second-Generation Antipsychotic-Treated Children
Bibliographic record
Abstract
OBJECTIVE: To determine whether implementation of a metabolic monitoring training program (MMTP) in an urban community-based setting improved monitoring in children treated with second-generation antipsychotics (SGAs) and changed prescription rates of SGAs to children. METHOD: The MMTP was implemented in the Vancouver Coastal Health Child and Youth Mental Health Teams (CYMHTs) on January 1, 2009. A retrospective review of paper charts and electronic records for children seen at the CYMHTs from September 1, 2007, to May 1, 2010, was performed to collect the following data: age, sex, foster care, immigrant status, Axis I diagnosis, and medications. In SGA-treated children, anthropometric measurements and blood work completed at baseline and 3, 6, and 12 months were also collected. RESULTS: Among the 1114 children seen pre-MMTP and 1262 children seen post-MMTP implementation, 174 (15.4%) and 81 (6.4%), respectively, were SGA-treated (P < 0.001). Among the SGA-treated children seen at the CYMHTs after MMTP implementation, 38.3% had a copy of the MMTP in their paper chart. Metabolic monitoring increased by up to 40% at baseline (P < 0.01), 20% at 3 (P < 0.01) and 6 months (P < 0.01), and 18% at 12 months after MMTP implementation. CONCLUSIONS: Implementation of an MMTP was associated with significantly improved monitoring rates of anthropometric and blood work parameters at baseline and the 3- and 6-month time points, with a trend for improvement at the 12-month time point, in SGA-treated children cared for in urban community mental health clinics. In addition, a 56% decrease in SGA prescriptions was observed following MMTP implementation in this population.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".