Postoperative pain management in patients with chronic kidney disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a health care problem with increasing prevalence worldwide. Pain management represents one of the challenges in providing perioperative care for this group of patients. Physicians from different specialties may be involved in pain management of CKD patients, especially in advanced stages. It is important to understand the clinical staging of kidney function in CKD patients as the pharmacotherapeutic pain management strategies change as kidney function becomes progressively impaired. Special emphasis should be placed on dose adjustment of certain analgesics as well as prevention of further deterioration of renal function that could be induced by certain classes of analgesics. Chronic pain is a common finding in CKD patients which may be caused by the primary disease that led to kidney damage or can be a direct result of CKD and hemodialysis. The presence of chronic pain in some of the CKD patients makes postoperative pain management in these patients more challenging. This review focuses on the plans and challenges of postoperative pain management for patient at different stages of CKD undergoing surgical intervention to provide optimum pain control for this patient population. Further clinical studies are required to address the optimal medication regimen for postoperative pain management in the different stages of CKD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".