Monitoring of metabolic adverse events of second-generation antipsychotics in a naive paediatric population followed in mental health outpatient and inpatient clinical settings: MEMAS prospective study protocol
Bibliographic record
Abstract
INTRODUCTION: Second-generation antipsychotics (SGAs) are widely used in the paediatric population. It is currently established that SGAs may induce metabolic adverse events (AEs) such as weight gain, perturbation of blood lipids or glucose with risk of potential cardiovascular morbidity and mortality. The Canadian Alliance for Monitoring Effectiveness and Safety of Antipsychotics in children (CAMESA) has published recommendations for monitoring the metabolic AEs of SGAs. Factors that may be associated with the onset of SGA's metabolic AEs and long-term consequences are less studied in the literature. The objectives of our research are to evaluate some factors that can influence the development of the SGA's metabolic AEs and to study clinical adherence to CAMESA guidelines. METHODS AND ANALYSIS: The Monitoring des Effets Métaboliques des Antipsychotiques de Seconde Génération study is a multicenter, prospective, longitudinal observational study with repeated measures of metabolic monitoring over 24 months. Two recruiting centres have been selected for patients under 18 years of age, previously naive of antipsychotics, starting an SGA or who have started an SGA for less than 4 weeks regardless of the diagnosis that motivated the prescription. Assessments are performed for anthropometric measures, blood pressure, blood tests at baseline and 1, 2, 3, 6, 9, 12 and 24 months of follow-up. ETHICS AND DISSEMINATION: The study protocol was approved by the CHU Sainte-Justine's Research Ethics Board (MP-21-2016-1201) in 2016 and obtained institutional suitability for the 'Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal' Research Center in May 2018. For all participants, written consent will be obtained from parents/caregivers as well as the participant's assent in order to enable their participation in this research project. The results of this research will be published. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov (number NCT04395326).
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".