Effect of serotonin modulating pharmacotherapies on body mass index and dysglycaemia among children and adolescents: a systematic review and network meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: Serotonin-modulating medications are commonly prescribed for mental health issues. Currently, there is limited consensus on weight gain and dysglycaemia development among children using these medications. The objective of this study is to review and synthesise all the available evidence on serotonin-modulating medications and their effects on body mass index (BMI), weight and glycaemic control. METHODS AND ANALYSIS: We will conduct a systematic review of all randomised controlled trials evaluating the use of serotonin-modulating medications in the treatment of children 2-17 years with mental health conditions. The outcome measures are BMI, weight and dysglycaemia. We will perform literature searches through Ovid Medline, Ovid Embase, PsycINFO and grey literature resources. Two reviewers from the team will independently screen titles and abstracts, assess the eligibility of full-text trials, extract information from eligible trials and assess the risk of bias and quality of the evidence. Results of this review will be summarised narratively and quantitatively as appropriate. We will perform a multiple treatment comparison using network meta-analysis to estimate the pooled direct, indirect and network estimate for all serotonin-modulating medications on outcomes if adequate data are available. ETHICS AND DISSEMINATION: Serotonin-modulating medications are widely prescribed for children with mental health diseases and are also used off-label. This network meta-analysis will be the first to assess serotonin modulating antidepressants and their effects on weight and glycaemic control. We anticipate that our results will help physicians and patients make more informed choices while considering the side effect profile. We will disseminate the results of the systematic review and network meta-analysis through peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42015024367.
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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 | 0.069 | 0.109 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 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".