The efficiency of potassium removal during bicarbonate hemodialysis
Bibliographic record
Abstract
Patients on chronic hemodialysis often portray high serum [K+]. Although dietary excesses are evident in many cases, in others, the cause of hyperkalemia cannot be identified. In such cases, hyperkalemia could result from decreased potassium removal during dialysis. This situation could occur if alkalinization of body fluids during dialysis would drive potassium into the cell, thus decreasing the potassium gradient across the dialysis membrane. In 35 chronic hemodialysis patients, we compared two dialysis sessions performed 7 days apart. Bicarbonate or acetate as dialysate buffers were randomly assigned for the first dialysis. The buffer was switched for the second dialysis. Serum [K+], [HCO3-], and pH were measured in samples drawn before dialysis; 60, 120, 180, and 240 min into dialysis; and 60 and 90 min after dialysis. The potassium removed was measured in the dialysate. During the first 2 hr, serum [K+] decreased equally with both types of dialysates but declined more during the last 2 hr with bicarbonate dialysis. After dialysis, the serum [K+] rebounded higher with bicarbonate bringing the serum [K+] up to par with acetate. The lower serum [K+] through the second half of bicarbonate dialysis did not impair potassium removal (295.9 +/- 9.6 mmol with bicarbonate and 299.0 +/- 14.4 mmol with acetate). The measured serum K+ concentrations correlated with serum [HCO3-] and blood pH during bicarbonate dialysis but not during acetate dialysis. Alkalinization induced by bicarbonate administration may cause redistribution of K during bicarbonate dialysis but this does not impair its removal. The more marked lowering of potassium during bicarbonate dialysis occurs late in dialysis, when exchange is negligible because of a low gradient.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".