Managing the symptom burden associated with maintenance dialysis: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
Individuals with kidney failure undergoing maintenance dialysis frequently report a high symptom burden that can interfere with functioning and diminish life satisfaction. Until recently, the focus of nephrology care for dialysis patients has been related primarily to numerical targets for laboratory measures, and outcomes such as cardiovascular disease and mortality. Routine symptom assessment is not universal or standardized in dialysis care. Even when symptoms are identified, treatment options are limited and are initiated infrequently, in part because of a paucity of evidence in the dialysis population and the complexities of medication interactions in kidney failure. In May of 2022, Kidney Disease: Improving Global Outcomes (KDIGO) held a Controversies Conference-Symptom-Based Complications in Dialysis-to identify the optimal means for diagnosing and managing symptom-based complications in patients undergoing maintenance dialysis. Participants included patients, physicians, behavioral therapists, nurses, pharmacists, and clinical researchers. They outlined foundational principles and consensus points related to identifying and addressing symptoms experienced by patients undergoing dialysis and described gaps in the knowledge base and priorities for research. Healthcare delivery and education systems have a responsibility to provide individualized symptom assessment and management. Nephrology teams should take the lead in symptom management, although this does not necessarily mean taking ownership of all aspects of care. Even when options for clinical response are limited, clinicians should focus on acknowledging, prioritizing, and managing symptoms that are most important to individual patients. A recognized factor in the initiation and implementation of improvements in symptom assessment and management is that they will be based on locally existing needs and resources.
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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 | 0.060 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.015 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".