Antidepressant and antipsychotic drug prescribing and complications of diabetes: a systematic review of observational studies
Bibliographic record
Abstract
Abstract Aims Psychotropic medication may be associated with adverse effects, particularly in people with diabetes. We conducted a systematic review of observational studies investigating the association between antidepressant or antipsychotic drug prescribing and diabetes outcomes. Methods We systematically searched PubMed, EMBASE, and PsycINFO to 15 th August 2022 to identify eligible studies. We used the Newcastle-Ottawa scale to assess study quality and performed a narrative synthesis. Results We included 18 studies, 14 reporting on antidepressants and four on antipsychotics. There were 11 cohort studies, one self-controlled before and after study, two case-control studies, and four cross-sectional studies, of variable quality and highly heterogeneous in terms of study population, exposure definition and outcome analysed. Antidepressant prescribing may be associated with increased risk of macrovascular outcomes, whilst evidence on antidepressant and antipsychotic prescribing and glycaemic control was mixed. Few studies reported on microvascular complications and cardiometabolic factors other than glycaemic control and just one study reported on antipsychotics and diabetes complications. Conclusions There has been little study of antidepressant and antipsychotic drug prescribing in relation to diabetes outcomes. Further, more methodologically robust, research is needed to inform and enhance antidepressant and antipsychotic drug prescribing and monitoring practices in people with diabetes.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".