The effects of cannabinoids on the kidney
Bibliographic record
Abstract
-tetrahydrocannabinol (THC), cannabidiol (CBD), spice, and 2-arachidonoylglycerol (2-AG). With more than 100 cannabinoids identified, their influence on the nervous system, role in pain management, and effects due to illicit use have been extensively studied. However, their effects on peripheral organs, such as the kidneys, require further examination. With dramatic rises in use, production, and legalization, it is essential to understand the impact and mechanistic properties of these drugs as they pertain to renal and cardiovascular physiology. The goal of this review is to summarize prior literature on the expression of cannabinoid receptors and how cannabinoids influence renal function. This review first discusses the interaction of the endocannabinoid system (ECS) and renal physiology and pathophysiology. Following, we briefly discuss the role of the ECS in various kidney diseases and the potential therapeutic applications of drugs targeting the cannabinoid system. Lastly, recent studies have identified several detrimental effects of cannabinoids, not only on the kidney but also in contributing to adverse cardiovascular outcomes. Thus, the negative impact of cannabinoids on renal function and the development of various cardiovascular diseases is also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".