Water Treatment in the Home Hemodialysis Setting: Use of Recirculators Decrease Bacteria and Endotoxin Contamination
Bibliographic record
Abstract
Water for home hemodialysis is treated using carbon filtration and a series of deionization (DI) tanks. These systems are practical and insure quality product water. The main drawback is susceptibility to bacterial contamination and growth. In an effort to control bacterial growth and endotoxin production in our training rooms and patient homes, our DI systems are being equipped with recirculators consisting of a pump and a flow switch. The pump maintains water in constant recirculation through the DI tanks and lines. This pump was selected to prevent overheating of the water in the system during recirculation, thus helping to prevent bacterial growth. The flow switch is a safety device that stops the pump if the flow rate drops below a preset value while also preventing the pump from overheating should the system develop a leak. The plumbing of this system prevents untreated water from bypassing the DI tanks, while the water quality remains monitored at the product port. We began using recirculators in March of this year in our training rooms. Our results have improved from 7 sets of cultures requiring an action, per AAMI standards, in 8 patient months prior to recirculator installation, to the need to re-sanitize one system, one time since recircultor installation during the ensuing 11 patient months. We currently have 3 recirculator systems (out of 8 DI home systems) in patient homes. One patient on rural water had AAMI action levels with 8 sets of cultures since January, 2002, prior to recirculator installation. He has experienced no action level needs since installation in early August. Aside from home installation problems with the initial patient, recircultors have resulted in no AAMI action levels over the past 6 patient months.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".