Prediction of Reduction in Predialysis Concentrations due to Interdialysis Weight Gain
Bibliographic record
Abstract
There is little quantitative information about the influence of weight change before and during hemodialysis on the concentration of proteins, lipoproteins, lipids, enzymes and other dialysis-resistant compounds in blood. We studied the concentration of 12 such compounds before and at the end of high-flux hemodialyses, 1.5 h after the start and 1, 2 and 3 h postdialysis and have developed formulae for roughly predicting the near steady-state 2-3 h postdialysis concentration. For hemoglobin, albumin, total protein and total cholesterol, the relationship of mean change in concentration to weight loss in groups was linear, and the % increase in concentration correlation correlated with % weight reduction (r = 0.64-0.81 and p = 0.002-0.0002). Correlations with ultrafiltration rate were comparable. By 3 h postdialysis values were relatively stable; the average fall in concentration for theses 4 compounds was 25% from end dialysis. The simplest formula we found which roughly predicts the % increase in concentration from predialysis to 3 h postdialysis is to multiply the % loss in body weight in kg during dialysis by 3.3. More accurate formulae were developed using combined and specific regression equations relating % weight loss during dialysis to % concentration rise. Mean values for alkaline phosphatase, triglycerides, lipoprotein (a), high-density lipoprotein cholesterol, calcium, apolipoprotein B, bilirubin and aspartate aminotransferase also rose appreciably during dialysis with significant increases for the first five. With major interdialytic weight gain, the reduction in predialysis concentrations of hemoglobin and cholesterol may be enough to inappropriately modify treatment decisions about anemia (e.g. erythropoietin) or hypercholesterolemia, and to cause false concern about the concentration of albumin for nutrition and prognosis. Major weight gain may also contribute to concentration changes in numerous other compounds resistant to dialysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".