Treating mineral metabolism disorders in patients undergoing long hemodialysis: A search for an optimal strategy
Bibliographic record
Abstract
In hemodialysis (HD) patients, mineral metabolism (MM) disorders have been associated with an increased mortality rate. We report the evolution of MM parameters in a stable HD population undergoing long hemodialysis by performing an annual cross-sectional analysis for every year from 1994 to 2008. The therapeutic strategy has changed: the dialysate calcium concentration has decreased from a mean of 1.7 +/- 0.1 to 1.5 +/- 0.07 mmol/L and has been adapted to parathyroid hormone serum levels (from 1 to 1.75 mmol/L). The use of calcium-based and aluminum-based phosphate binders has decreased and they have been replaced by sevelamer; alfacalcidol has partly been replaced by native vitamin D. The percentage of patients with a parathyroid hormone serum level between 150 and 300 pg/mL has increased from 9% to 67% (P<0.001); the percentage of patients with phosphataemia between 1.15 and 1.78 mmol/L has increased from 39% to 84% (P<0.001). The percentage of those with albumin-corrected calcemia between 2.1 and 2.37 mmol/L has increased from 29% to 61% (P<0.001), and that of patients with a calcium-phosphorous product (Ca x P) level >4.4 mmol/L decreased from 8.8% to 2% (P=0.02). Although patients undergo long and intensive HD treatment, MM disorders are common. However, an appropriate strategy, mostly consisting of native vitamin D supplementation, progressive replacement of calcium-based phosphate binders with non-calcium-based ones, and individualization of dialysis session duration and dialysate calcium concentration, would result in a drastic improvement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".