A Review of Phosphate Binders in Chronic Kidney Disease: Incremental Progress or Just Higher Costs?
Bibliographic record
Abstract
As kidney disease progresses, phosphorus retention also increases, and phosphate binders are used to treat hyperphosphatemia. Clinicians prescribe phosphate binders thinking that reducing total body burden of phosphorus may decrease risks of mineral and bone disorder, fractures, cardiovascular disease, progression of kidney disease, and mortality. Recent meta-analyses suggest that sevelamer use results in lower mortality than use of calcium-containing phosphate binders. However, studies included in meta-analyses show significant heterogeneity, and exclusion or inclusion of specific studies alters results. Since no long-term studies have been conducted to determine whether treatment with any phosphate binder is better than placebo on any hard clinical endpoint (including mortality), it is unclear whether possible benefit with sevelamer represents net benefit of sevelamer, net harm with calcium-containing phosphate binders, or both. Although one meta-analysis suggested that calcium acetate may be more efficacious gram for gram than calcium carbonate as a binder, calcium acetate did not reduce hypercalcemia, and gastrointestinal intolerance was higher. Data are insufficient to determine whether calcium acetate provides lower risk of vascular calcification than calcium carbonate. Fears of lanthanum accumulation in the central nervous system or bone with long-term treatment do not appear to be warranted. Newer iron-containing phosphate binders have potential benefits, such as lower pill burden (sucroferric oxyhydroxide) and improved iron parameters (ferric citrate). The biggest challenge to phosphate binder efficacy is non-adherence. This article reviews the current knowledge regarding safety, effectiveness, and adherence with currently marketed phosphate binders and those in development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".