Current Views on Dopaminergic Drugs Affecting Glucose Homeostasis
Bibliographic record
Abstract
BACKGROUND: For more than three decades, it has been known that manipulation of dopaminergic system could affect glucose homesotasis in experimental animals. The notion that glucose homeostasis in human might be influenced by dopaminergic drugs has attracted a great deal of attention in the past two decades. In spite of rapid advancements in revealing involvement of dopaminergic neurotransmission in insulin release, glucose up-take and pancreatic beta cell function in general through centrally and peripherally controlled mechanisms, there are discrepancies among observations on experimental animals and human subjects. CONCLUSION: With the expansion of pharmacotherapy in psychotic conditions, depression and endocrine abnormalities along with a sharp increase in prevalence of type two diabetes and disturbances of glucose homeostasis as a major risk factor for many cardiovascular complications and associated mortalities; it seems a critical analysis of recent investigations on drugs which act as agonists or antagonists of dopaminergic receptors in various tissues and organs may provide better insight into how safe and efficient these medicines could be prescribed. Furthermore, the other main objective of present review is to compare clinical data on significance of changes in blood glucose and insulin levels during short term and after long term treatment with these agents. This in turn would be beneficial for determining adequate strategies to combat or to avoid adverse effects associated with dopaminergic drug therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".