Quality of water in hemodialysis centers in <scp>B</scp>aghdad, <scp>I</scp>raq
Bibliographic record
Abstract
Dialysis water quality is one of the important parameters all over the world because of its direct influence on the health of kidney patients. In Iraq, there are more than 20 dialysis centers; most of them contain identical units for the production of dialysis water. In this work, the quality of water used for dialysis in six dialysis centers located within Baghdad hospitals was evaluated. Samples of product water from each of the six dialysis centers were examined for total heterotrophic bacteria, endotoxin, and chemical contaminants. Endotoxin was measured on-site using a portable instrument. Bacteriological and chemical examinations were done in the laboratory after collecting samples from each dialysis center. The results showed a fluctuation in the produced water quality that makes the produced water unaccepted when compared with international standards. Bacterial counts for 60% of the analyzed samples were above the action level (50 colony-forming units[CFU]/mL), while five out of the six dialysis centers showed values higher than the maximum value (100 CFU/mL). Chemical analysis showed that the dialysis water quality suffers from elevated aluminum concentration for all dialysis centers. All hemodialysis centers need thorough monitoring and preventive maintenance to ensure good water quality. In addition, it is important to revise the design of the water treatment units according to the feed and product water quality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".