Using water wisely: New, affordable, and essential water conservation practices for facility and home hemodialysis
Bibliographic record
Abstract
Despite a global focus on resource conservation, most hemodialysis (HD) services still wastefully or ignorantly discard reverse osmosis (R/O) "reject water" (RW) to the sewer. However, an R/O system is producing the highly purified water necessary for dialysis, it rejects any remaining dissolved salts from water already prefiltered through charcoal and sand filters in a high-volume effluent known as RW. Although the RW generated by most R/O systems lies well within globally accepted potable water criteria, it is legally "unacceptable" for drinking. Consequently, despite being extremely high-grade gray water, under current dialysis practices, it is thoughtlessly "lost-to-drain." Most current HD service designs neither specify nor routinely include RW-saving methodology, despite its simplicity and affordability. Since 2006, we have operated several locally designed, simple, cheap, and effective RW collection and distribution systems in our in-center, satellite, and home HD services. All our RW water is now recycled for gray-water use in our hospital, in the community, and at home, a practice that is widely appreciated by our local health service and our community and is an acknowledged lead example of scarce resource conservation. Reject water has sustained local sporting facilities and gardens previously threatened by indefinite closure under our regional endemic local drought conditions. As global water resources come under increasing pressure, we believe that a far more responsible attitude to RW recycling and conservation should be mandated for all new and existing HD services, regardless of country or region.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".