Phosphate removal model: An observational study of low‐flux dialyzers in conventional hemodialysis therapy
Bibliographic record
Abstract
Precise assessing phosphate removal by hemodialysis (HD) is important to improve phosphate control in patients on maintenance HD. We reported a simple noninvasive model to estimate phosphate removal within a 4-hour HD. One hundred sixty-five patients who underwent HD 4 hours per session using low-flux dialyzers made of polysulfone (1.2 m(2)) or triacetate (1.3 m(2)) were enrolled. Blood flows varied from 180 to 300 mL/min. Effluent dialysate samples were collected during the 4-hour HD treatment to measure the total phosphate removal. Predialysis levels of serum phosphate, potassium, hematocrit, intact parathyroid hormone, total carbon dioxide (TCO(2)), alkaline phosphatase, clinical and dialysis characteristics were obtained. One hundred thirty-five observations were randomly selected for model building and the remaining 30 for model validation. Total amount of phosphate removal within the 4-hour HD was mostly 15-30 mmol. A primary model (model 1) predicting total phosphate removal was Tpo(4) = 79.6 × C(45) (mmol/L) - 0.023 × age (years) + 0.065 × weight (kg) - 0.12 × TCO(2) (mmol/L) + 0.05 × clearance (mL/min) - 3.44, where C(45) was phosphate concentration in spent dialysate measured at the 45 minute of HD and clearance was phosphate clearance of dialyzer in vitro conditions offered by manufacturer's data sheet. Since the parameter TCO(2) needed serum sample for measurement, we further derived a noninvasive model (model 2):Tpo(4) = 80.3 × C(45) - 0.024 × age + 0.07 × weight + 0.06 × clearance - 8.14. Coefficient of determination, root mean square error, and residual plots showed the appropriateness of two models. Model validation further suggested good and similar predictive ability of them. This study derived a noninvasive model to predict phosphate removal. It applies to patients treated by 4-hour HD under similar conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".