Blood pressure and volume management in dialysis: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
Blood pressure (BP) and volume control are critical components of dialysis care and have substantial impacts on patient symptoms, quality of life, and cardiovascular complications. Yet, developing consensus best practices for BP and volume control have been challenging, given the absence of objective measures of extracellular volume status and the lack of high-quality evidence for many therapeutic interventions. In February of 2019, Kidney Disease: Improving Global Outcomes (KDIGO) held a Controversies Conference titled Blood Pressure and Volume Management in Dialysis to assess the current state of knowledge related to BP and volume management and identify opportunities to improve clinical and patient-reported outcomes among individuals receiving maintenance dialysis. Four major topics were addressed: BP measurement, BP targets, and pharmacologic management of suboptimal BP; dialysis prescriptions as they relate to BP and volume; extracellular volume assessment and management with a focus on technology-based solutions; and volume-related patient symptoms and experiences. The overarching theme resulting from presentations and discussions was that managing BP and volume in dialysis involves weighing multiple clinical factors and risk considerations as well as patient lifestyle and preferences, all within a narrow therapeutic window for avoiding acute or chronic volume-related complications. Striking this challenging balance requires individualizing the dialysis prescription by incorporating comorbid health conditions, treatment hemodynamic patterns, clinical judgment, and patient preferences into decision-making, all within local resource constraints.
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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 | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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".