Thinking Volume First: Developing a Multifaceted Systematic Approach to Volume Management in Hemodialysis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Volume overload and hypovolemia-induced symptoms are common in the hemodialysis (HD) population and frequently result in emergency department visits and hospitalization. A structured strategy for the reporting, evaluation, and management of disordered volume status may improve clinical outcomes and the patient experience. We developed a new strategy that systematically addresses volume issues by leveraging the electronic medical record, technological adjuncts, and multidisciplinary expertise to institute new processes of care in our HD unit. SOURCES OF INFORMATION: This initiative was implemented in a unit located in an urban academic hospital where 250 patients receive maintenance HD. This initiative involved a multidisciplinary team of health professionals including physicians, nurse practitioners, social workers, and dieticians. METHODS: We generated volume metrics for HD recipients based on routinely collected data from the unit's electronic medical record. We then engaged stakeholders in a root cause analysis to identify the major causes of abnormal volume metrics locally. We subsequently developed interventions that were designed to address each of the major causes in a pragmatic and sustainable program. KEY FINDINGS: The final product was a local volume management program with 3 components. First, we integrated volume metric reporting into the routine surveillance bloodwork reports across our unit. This enabled the clinical teams to more easily target patients at risk for volume-related adverse events and provide them with closer surveillance. Those identified with abnormal volume metrics were then evaluated with the use of technologic adjuncts such as lung ultrasound and bioimpedance spectroscopy to complement traditional assessments of volume status. Finally, those with abnormal volume metrics underwent rigorous interdisciplinary review for potential nutritional/social interventions. LIMITATIONS: While we report the successful initial implementation of the program within a single center, it remains unclear whether this initiative will lead to meaningful benefits for HD recipients, be readily applicable in other centers, or be sustainable in the long term. IMPLICATIONS: This volume management program will need further evaluation linked to outcome assessment and feasibility in other centers before wider adoption is advocated.
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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 | 0.176 | 0.421 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".