Bibliographic record
Abstract
Abstract The changing pattern of pharmaceutical use in dialysis patients has resulted in several alterations to dialysate calcium concentration over the past 40 years. Non‐calcium–containing phosphate binders and calcimimetics are the most recent examples of drugs that influence the overall calcium balance in dialysis patients. Renal osteodystrophy, vascular disease, and mortality are believed to be linked in patients with chronic kidney disease (CKD), although to date most of the evidence is based only on statistical associations. The precise pathophysiology of vascular calcification in end‐stage renal disease is unknown, but risk factors include age, hypertension, time on dialysis, and, most significantly, abnormalities in calcium and phosphate balance. Prospective studies are required before “cause and effect” can be established with certainty, but it is an active metabolic process with inhibitors and promoters. Serum calcium levels are clearly influenced by dialysate calcium and may therefore play an important role in influencing vascular calcification. Clinical management of hyperphosphatemia is being made easier by the introduction of potent non‐calcium–based oral phosphate binders such as lanthanum carbonate. Short‐term and long‐term studies have demonstrated its efficacy and safety. Vitamin D analogs have been a disappointment in the control of serum parathyroid hormone (PTH) levels, but evidence is emerging that vitamin D has other important metabolic effects apart from this, and may confer survival advantages to patients with CKD. Calcimimetics such as cinacalcet enable much more effective and precise control of PTH levels, but at the cost of a major financial burden. While it is unreasonable to expect that any one of these recent pharmacological developments will be a panacea, they provide researchers with the tools to begin to examine the complex interplay between calcium, phosphate, vitamin D, and PTH, such that further progress is fortunately inevitable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".