Aluminio en pacientes con terapia de reemplazo renal crónico con hemodiálisis en dos unidades renales en Bogotá
Bibliographic record
Abstract
OBJECTIVE: Determining aluminium concentrations in the serum of patients undergoing chronic renal replacement therapy with haemodialysis and concentration in distribution network water and dialysis in two renal units in Bogotá. MATERIAL AND METHODS: This was a descriptive study of 63 haemodialysed patients and 20 healthy subjects. Aluminium concentration was determined in water and serum using graphite furnace atomic absorption spectrometry with deuterium lamp background corrector. RESULTS: Average aluminium concentration was 26.5 µg/L in patients (ranging from 11.2 to 49.2 µg/L; 8.03 standard deviation) and 8.05 µg/L in healthy individuals (ranging from undetectable to 17.2 µg/L; 4.31 standard deviation). Aluminium concentration in dialysis water and distribution network water was below 2 µg/L and 200 µg/L, respectively. CONCLUSIONS: Aluminium concentration in water and serum in this study was below international standard values, thereby indicating appropriate treatment. Additionally, aluminium concentration in pre-HD and post-HD sera was below that reported previously. Aluminium hydroxide uptake increases aluminium concentration in serum. Personal situation regarding age, gender, civil and work status were not risk factors determining aluminium concentrations in serum.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".