Optimizing the dialysate calcium concentration in bicarbonate haemodialysis
Bibliographic record
Abstract
BACKGROUND: There is no consensus regarding the optimal dialysate calcium concentration (DCa) during haemodialysis (HD). Low DCa may predispose to acute arrhythmias, whereas high DCa increases the long-term risk of soft tissue calcifications. METHODS: Twenty-two HD patients treated in four dialysis centres underwent two HD sessions, respectively, with 1.5 and 1.25 mmol/L total DCa. Calcium mass balance (CMB) was calculated from ionized calcium (iCa) in the dialysate and blood at the start and end of each run, using a kinetic formula to define the mean concentrations in the blood and dialysate and then estimating CMBs over the entire treatments. RESULTS: Mean blood iCa levels increased using 1.5 DCa, whereas they remained unchanged using 1.25 DCa. Diffusive CMB positively correlated with the dialysate/blood iCa gradient. With 1.5 DCa, diffusive CMBs were strongly positive at the blood side and negative at the dialysate side, indicating transfer from dialysate to blood. With 1.25 DCa, despite a negative dialysate/blood iCa gradient, diffusive CMB was slightly positive in blood and negative in dialysate. The global balances based on both the convective and diffusive components showed a positive net transfer of Ca from dialysate to blood with 1.5 DCa and an approximately neutral Ca flux with 1.25 DCa. CONCLUSIONS: While CMB is nearly neutral when using 1.25 DCa, the use of 1.5 DCa results in a gain of Ca during HD. The risks associated with Ca load should be considered in the choice of DCa prescription for HD but need also be weighed against the risk of worse haemodynamic dialysis tolerance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".