Management of pain in end‐stage renal disease patients: Short review
Bibliographic record
Abstract
Pain management in end stage renal disease (ESRD) patients is a complex and challenging task to accomplish, and effective pain and symptom control improves quality of life. Pain is prevalent in more than 50% of hemodialysis patients and up to 75% of these patients are treated ineffectively due to its poor recognition by providers. A good history for PQRST factors and intensity assessment using visual analog scale are the initial steps in the management of pain followed by involvement of palliative care, patient and family counseling, discussion of treatment options, and correction of reversible causes. First line should be conservative management such as exercise, massage, heat/cold therapy, acupuncture, meditation, distraction, music therapy, and cognitive behavioral therapy. Analgesics are introduced according to WHO guidelines (by the mouth, by the clock, by the ladder, for the individual, and attention to detail) using three-step analgesic ladder model. Neuropathic pain can be controlled by gabapentin and pregabalin. Substitution/addition of opioid analgesics are indicated if pain control is not optimal. Commonly used opioids in ESRD patients are tramadol, oxycodone, hydromorphone, fentanyl, methadone, and buprenorphine. Methadone, fentanyl, and buprenorphine are the ideal analgesics in ESRD. However, complex pain syndrome requires multidrug analgesic regimen comprising opioids, non-opioids, and adjuvant medication, which should be individualized to the patient to achieve adequate pain control.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".