Personal viewpoint: Hemodialysis—Water, power, and waste disposal: Rethinking our environmental responsibilities
Bibliographic record
Abstract
While medical health professionals are trained to detect, treat, and comfort, they are not trained to consider the environmental impact of the services they provide. Dialysis practitioners seem particularly careless in the use of natural resources—especially water and power—and seem broadly ignorant of the profound medical waste issues created by single use dialysis equipment. If the data we have collected is an indication, then extrapolation of this data to a dialysis population currently estimated at ~2 million patients worldwide, a “world dialysis service” would use ~156 billion liters of water and discard ~2/3 of that during reverse osmosis. This waste occurs, despite the discarded water being high-grade “gray water” of potable standard. The same world dialysis service would consume 1.62 billion kWh of power—mostly generated from coal and other environmentally damaging sources. Our world dialysis service, based on ~2 kg of waste from each dialysis treatment, would generate ~625,000 tonnes of plastic waste—waste that would be potentially reusable if simple sterilizing techniques were applied to it at the point of generation. Dialysis services must begin to explore eco-dialysis potentials. The continued plundering of resources without considering reuse or recycling, exploration of renewable energy options, or the reduction of the carbon footprint of the dialysis process . . . is unsustainable. Sustainable dialysis practices should be a global goal in the coming decade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".