Microbial quality of hemodialysis water, a survey of six centers in <scp>L</scp>agos, <scp>N</scp>igeria
Bibliographic record
Abstract
Patients with end-stage kidney disease (ESRD) on maintenance hemodialysis (HD) are usually exposed to large volumes of dialysate, which is separated from patients' blood only by thin membrane of dialyzer. It is therefore essential to frequently monitor the quality of HD water to ensure that it meets the recommended standards. The objective of this study was to evaluate the microbial quality of HD water in Lagos, Nigeria. Four sets of pre- and post-treatment water samples, 20 mL each, were collected from six HD centers in Lagos and tested for microbial contamination using the molten Tryptic soy agar in accordance with Association for Advancement of Medical Instrumentation (AAMI) and European Best Practice Guidelines (EBPG). Pyrogen tests were also conducted on pre- and post-treatment samples using standard technique. Information on water treatment modalities, maintenance practices and quality control measures in each center were obtained using a questionnaire. All centers use treated water for HD purpose. None of the HD centers met EBPG/AAMI guidelines for microbial contaminants as the mean levels of Escherichia coli in both feed and treated water were 441.7 ± 87.90 and 168.5 ± 64.03, respectively. E. coli was the commonest organism isolated in both feed and treated water in all the centers. HD water quality is still a neglected problem in our environment and more efforts are required to ensure good water quality for HD purpose.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".