The Present and Future of Pain Management in Patients With Chronic Kidney Disease: A Narrative Review
Bibliographic record
Abstract
Chronic kidney disease (CKD) is an increasingly common ailment, greatly affecting the quality of life, morbidity, and mortality of the population. In addition, more than half of patients with CKD experience chronic pain, mostly of nociceptive or neuropathic origin, and physicians frequently have to find a difficult balance between safety and efficacy to control it. The altered metabolism and renal excretion in patients with CKD modify the pharmacokinetics and pharmacodynamics of several analgesic drugs, making this population more prone to side effects and lower efficacy, since the doses often need to be reduced. This narrative review describes the current pharmacological approaches for nociceptive and neuropathic pain, and emerging alternatives, such as cannabinoids and low-dose naltrexone. We also describe the current knowledge on anesthesia, perioperative and acute pain management, and injectables, including ketamine and corticosteroids (intra-articular and epidural). In the variable and possibly deteriorating clinical context of CKD, this review shows that pain management needs to be individualized and carefully discussed with the patient; close monitoring is also necessary to adjust the treatment and obtain effective pain control while minimizing the risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".