Bibliographic record
Abstract
Pharmacokinetic studies conducted in patients with CRF demonstrate that the nonrenal clearance of multiple drugs is reduced. Although the mechanism by which this occurs is unclear, several studies have shown that CRF affects the metabolism of drugs by inhibiting key enzymatic systems in the liver, intestine and kidney. The down-regulation of selected isoforms of the hepatic cytochrome P450 (CYP450) has been reported secondary to a decrease in gene expression. This is associated with major reductions in metabolism of drugs mediated by CYP450. The main hypothesis to explain the decrease in liver CYP450 activity in CRF appears to be the accumulation of circulating factors which can modulate CYP450 activity. Liver phase II metabolic reactions are also reduced in CRF. On the other hand, intestinal drug disposition is affected in CRF. Increased bioavailability of several drugs has been reported in CRF, reflecting decrease in either intestinal first-pass metabolism or extrusion of drugs (mediated by P-glycoprotein). Indeed, intestinal CYP450 is also down-regulated secondary to reduced gene expression, whereas, decreased intestinal P-glycoprotein activity has been described. Finally, although the kidneys play a major role in the excretion of drugs, it has the capacity to metabolize endogenous and exogenous compounds. CRF will lead to a decrease in the ability of the kidney to metabolize drugs, but the repercussions on the systemic clearance of drugs is still poorly defined, except for selected xenobiotics. In conclusion, reduced drug metabolism should be taken into account when evaluating the pharmacokinetics of drugs in patients with CRF.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".