Bibliographic record
Abstract
PURPOSE OF REVIEW: Strong epidemiological and pathologic evidence associates NSAIDs with kidney disease, both acute and chronic. Hence, the usage of NSAIDs has decreased in patients with, or at risk for, chronic kidney disease (CKD). Coupled with this has been a rise in use of opioids and other non-NSAID alternatives, which do come with significant, and underrecognized, risk of nonrenal adverse events. We review the literature to understand if this shift is appropriate or deleterious. RECENT FINDINGS: NSAIDs do have a low but tangible risk in causing acute kidney injury, electrolyte imbalances, and increasing blood pressure. However, their role in causing progressive kidney disease is due to long-term usage in high cumulative dosages, and the use of NSAIDs in combination with other agents. Alternatives such as opioids, tramadol, gabapentin and baclofen have weak evidence to support their use and strong evidence to show their harm in patients with CKD. SUMMARY: Tradeoffs are inherent in using active pharmaceuticals, and NSAIDs are no exception. Balancing potential benefits with possible adverse effects around pain management should be a part of every conversation for patients with kidney disease.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".