Quality of hemodialysis water in a resource‐poor country: The <scp>N</scp>igerian example
Bibliographic record
Abstract
Hemodialysis (HD) patients are exposed to large volumes of water, separated from patients' blood by semipermeable membrane of dialyzers. Chemical contaminants in poorly treated water impact negatively on quality of life of these patients. This survey was carried out to assess the HD water quality in Lagos, Nigeria. Ten milliliters of feed and treated water from all six HD centers in Lagos were tested for aluminum, copper, zinc, magnesium, calcium using atomic absorption spectrometry; sodium and potassium were tested using flame photometry, fluoride with molecular photoluminescence method; sulfate using turbidimetry, nitrates measured by cadmium column reduction method, chloramines and free chlorine were measured using N, N-diethyl-1-P-phenylenediamine colorimetric method. Information on sources of feed water, frequency of testing of HD water, methods of water treatment, type of water purification system and maintenance was also obtained. All centers used borehole as main source of water supply. None of the centers met Association for the Advancement of Medical Instrumentation (AAMI) guidelines for most chemical contaminants. Only chlorine (0.48 ± 0.07 mg/L) and potassium (3.9 ± 0.95 mg/L) levels met AAMI standards after treatment. Mean levels of chemical contaminants in treated water were as follows: aluminum 0.04 ± 0.01 mg/L, zinc 0.27 ± 0.08 mg/L, chloramines 0.16 ± 0.03 mg/L, fluoride 1.83 ± 0.40 mg/L, sulfate 117 ± 86.1 mg/L which were mildly elevated; calcium 126.00 ± 22.7 mg/L, sodium 179 ± 25.6 mg/L, and nitrate 70.5 ± 50.8 mg/L, which were markedly elevated; and magnesium 8.3 ± 3.38 mg/L, which was moderately elevated above AAMI recommended levels. HD water quality is poor in our environment. Concerted efforts are required to ensure good quality water for HD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".