Hemodialysis and Water Management in a Dialysis Unit in Morocco, an Approach to Dealing With Water Scarcity
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease is a global public health issue, affecting approximately 10% of the world's population, and more than 3 million people living with kidney failure who are estimated to be on maintenance dialysis programs, with the majority receiving hemodialysis (HD). This treatment is particularly water-intensive, posing a considerable challenge in regions experiencing water scarcity, such as Morocco. METHODS: Our HD center in Oujda, Eastern Morocco, has implemented several key strategies to address water scarcity and ensure uninterrupted HD procedures during periods of hydric stress. RESULTS: These strategies include expanding water storage capacities to safeguard against shortages, upgrading infrastructure to enhance water efficiency, and employing innovative technology for real-time monitoring and management of water resources. Additionally, we collaborate closely with local water authorities to secure reliable water supplies and explore possibilities for water regeneration and recycling. DISCUSSION: The rising demand for clean water, coupled with its increasing scarcity, presents a significant challenge for healthcare systems, particularly in the context of HD. Therefore, innovative approaches are essential to mitigate this issue. The concept of green dialysis, which focuses on reducing water usage and minimizing environmental impact, is emerging as a promising solution with measurable benefits. Implementing water-efficient reverse osmosis systems has resulted in significant reductions in water waste, while real-time monitoring and early warning systems have enhanced water security and operational efficiency. Additionally, initiatives exploring the reuse of reject water hold potential for further conservation. These tangible outcomes demonstrate how green dialysis practices can contribute to sustainable HD treatment, ensuring uninterrupted patient care, reduced resource consumption, and improved environmental stewardship in water-scarce regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".