Chromium: Rise and Shine in Peritoneal Dialysis Patients?
Bibliographic record
Abstract
Some trace elements are altered with chronic kidney disease. Selenium, zinc, and manganese tend to be wasted, and there is growing evidence that selenium deficiency is associated with mortality on dialysis. Other trace elements accumulate, such as chromium, cobalt, lead, molybdenum, and vanadium. The highest chromium levels are found in dialysis patients. The dialysis modality may further affect these levels, especially in hemodialysis patients, where even small contaminations in the dialysis feed water may lead to a concentration gradient that increases the concentration of certain trace elements. Chromium levels in peritoneal dialysis (PD) patients have been understudied. A single cross-sectional study found substantially higher chromium levels in PD patients. In that study, the chromium concentration in the spent dialysate decreased substantially, suggesting that PD fluid could be a source of chromium. Chromium-lactate complexes may have been formed, which are easily absorbed. In our center, we observed a decrease in chromium level when using physiological PD fluids. This review discusses the potential mechanisms and raises the question of whether this accumulation of chromium is unlikely to be associated with a beneficial outcome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".