Metformin for the Prevention of Antipsychotic-Induced Weight Gain: Guideline Development and Consensus Validation
Bibliographic record
Abstract
BACKGROUND: Overweight and obesity are highly prevalent in people with severe mental illness (SMI). Antipsychotic-induced weight gain (AIWG) is one of the most commonly reported and distressing side effects of treatment and people living with SMI place a high value on the avoidance of this side effect. Metformin is the most effective pharmacological intervention studied for the prevention of AIWG yet clear guidelines are lacking and evidence has not translated into practice. The aim of this research was to develop a guideline for the use of metformin for the prevention of AIWG. STUDY DESIGN: The appraisal of guidelines for research and evaluation II instrument (AGREE II) was followed for guideline development. Literature was reviewed to address key health questions. The certainty of evidence was evaluated using GRADE methodology and an evidence-to-decision framework informed the strength of the recommendations. A consensus meeting was held where the algorithm and strength of recommendations were agreed. An independent external review was conducted involving experts in the field, including patient and public partners. STUDY RESULTS: Metformin is the only pharmacological agent that has demonstrated efficacy for preventing AIWG. Co-commencement with antipsychotic medicines can reduce the extent of weight gain by 4.03 kg (95% CI -5.78 kg to -2.28 kg) compared to controls. A guideline for the use of metformin for the prevention of AIWG was developed with specific recommendations for co-commencement of metformin at initiation with an antipsychotic or commencement if certain criteria are present. Core recommendations were graded as strong by consensus agreement. CONCLUSIONS: This is the first published evidence-based guideline using the AGREE II framework and GRADE methods for the use of metformin to prevent AIWG incorporating recommendations for co-commencement. Implementation and evaluation of the guideline will be supported by a shared decision-making package and assessment of barriers and facilitators to implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".