Interventions to mitigate antipsychotic associated weight gain using an algorithmic measurement-based integrated clinical pathway-a retrospective chart review study
Bibliographic record
Abstract
Antipsychotics form an essential component in the treatment of several psychiatric disorders; they are, however, associated with weight gain and metabolic dysregulation resulting in significant psychological and physical challenges, leading to treatment non-adherence, poor quality of life, and increased cardiovascular morbidity. Several non-pharmacological and pharmacological interventions are available to mitigate antipsychotic-induced weight gain (AIWG). However, data on the utility of these interventions in real-world settings are limited. In this study, a retrospective chart review of patients attending the mental health and metabolism clinic that specializes in providing care for metabolic dysfunction in those with mental illness was conducted from 2016-2022. Charts of included patients were reviewed, and data pertaining to demographic, anthropometric and metabolic measures were collected and analyzed. The primary outcome measure was a change in weight at 3, 6, 9, and 12 months. A significant effect of the clinic on body weight over time was noted. The mean change in body weight was -1.54kg (SE:0.35, p<0.001), -1.79kg (SE:0.42, p<0.001), -3.5kg (SE:0.832, p<0.001) and -1.77kg (SE:0.77, p=0.35) at 3, 6, 9, and 12 months respectively. Initial evidence from our real-world clinical setting suggests that periodic monitoring and systematic administration of interventions are effective in reducing AIWG.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".