Renal adverse effects of nonsteroidal anti-inflammatory drugs
Bibliographic record
Abstract
NSAIDs depress prostaglandins synthesis through inhibition of COX-1 that is involved in maintaining cell integrity and COX-2 that, although presents particularly in the kidneys, is overexpressed in response to inflammation. Both the beneficial and side effects of NSAIDs are, therefore, through their inhibition of COX enzymes. Introduction of COX-2-selective inhibitors has improved the safety profile of the drugs with regard to their most common side effect which occurs at the gastrointestinal level but has not rendered them less cardio-nephrotoxic. Renal side effects of NSAIDs are rare, sometimes transient and often reversible upon drug withdrawal. The incident rate and the severity of the renal side effect, however, increase in patients with risk factors such as those with diabetes, heart failure, renal dysfunction and in the elderly. The side effects range from electrolyte retention and reduce glomerular filtration to nephritic syndrome and chronic renal failure. These effects are shared among NSAIDs with evidence of dose and exposure dependency. There is no known predictor for the nephrotoxicity. However, a relationship has been found between high plasma concentration and the renal adverse effect of NSAIDs. The usefulness of therapeutic drug monitoring in patients with risk factors needs to be explored.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.007 | 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".