Pain management and the use of opioids in adults with kidney failure receiving conservative kidney management
Bibliographic record
Abstract
Conservative kidney management (CKM) is an active treatment for kidney failure (KF) for people who will either not benefit from kidney replacement therapy (KRT), do not wish to pursue KRT, or do not have access to KRT. CKM aims to improve patients' quality-of-life through meticulous attention to symptom management. KF is associated with a high symptom burden globally that is experienced across age, sex, and race with chronic pain being one of the most severe and common symptoms. The delivery of CKM therefore requires the integration of effective pain management strategies. This review will provide a detailed insight into CKM globally and will offer an approach to pain management for people with KF who are receiving CKM. Specifically, this review will provide an overview of the clinical characteristics of people receiving CKM across both high and low resource settings and the epidemiology of pain in this population. While it will provide some high-level considerations for the non-pharmacologic management of pain, it will focus predominantly on pharmacologic approaches. This will include considerations of non-opioid analgesics and strategies for the use of opioids in people receiving CKM. Furthermore, we will explore global disparities in kidney care, CKM, and pain management resources, including access to opioids and will discuss some of the additional challenges faced in low resource settings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".