Pharmacological treatment of antipsychotic-induced dyslipidemia and hypertension
Bibliographic record
Abstract
Second-generation antipsychotics (SGAs) are associated with significant comorbid metabolic abnormalities. Adjunct medications may be prescribed to treat these metabolic side effects, but the evidence supporting this practice (especially for the management of antipsychotic-associated dyslipidemia and hypertension) is limited. The purpose of this review was to evaluate the effects of adjunct medications on triglyceride, total cholesterol, low-density lipoprotein, high-density lipoprotein, and blood pressure levels in participants taking SGAs for psychosis. Studies were systematically searched and evaluated. Studies were included for review if participants were taking SGAs and if lipid and/or blood pressure levels were included as outcome measures. Statins, conventional lipid-lowering agents, fluvoxamine, ramelteon, topiramate, valsartan, telmisartan, omega-3 fatty acids, metformin (including both immediate-release and extended-release formulations), and a combination of metformin-sibutramine seemed to have beneficial effects on lipid levels. Valsartan, telmisartan, and topiramate appeared to be effective for controlling increases in blood pressure. The literature on adjunct medications for the treatment of antipsychotic-associated dyslipidemia and hypertension is not exhaustive, and long-term randomized-controlled trials would offer valuable results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".