Impact of glucose levels on advanced glycation end products in hemodialysis
Bibliographic record
Abstract
The current obesity epidemic throughout the western world has resulted in a considerable increase in the condition Type II diabetes mellitus. Recently, the World Health Organization has predicted that the global prevalence of Type II will increase from 175 million patients in 2003 to over 350 million by 2030. One of the major consequences of this disorder is renal failure, which presents itself as chronic kidney disease, and can progress to end-stage renal disease. Once diagnosed, patients are generally treated using dialysis due to a shortage of kidney donors. The fundamental process of dialysis still requires improvement because the survival rate of these patients is relatively poor. This has resulted in considerable research into improvements in hemodialysis membranes, and the challenge to find more suitable marker(s) in assessing the efficacy of the dialysis process. A class of compounds highlighted as a possible accumulative toxin is advanced glycation end products or AGEs. This is an article regarding the impact of hemodialysis and hemodiafiltration on glucose and AGE levels within the body and the consequences of a chronic hyperglycemic condition. It also highlights the negative aspects of using dextrose in conventional dialysis solutions (an area that has already been identified by peritoneal dialysis clinicians as problematic). The review concludes by suggesting several possible topics of future research.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".