What is good about <scp>PD</scp> + <scp>HD</scp> combined therapy
Bibliographic record
Abstract
It is known that β(2) -microglobulin (β(2) -MG) concentration in peritoneal dialysis (PD) patients is inversely correlated to the residual renal function (RRF). With decreasing RRF, some PD patients may necessarily be treated with hemodialysis (HD) once a week, not only for removing excess water and small solutes, but also for removing much larger solutes such as β(2) -MG. In this study, a kinetic model allowed us to show what is good about PD + HD combined therapy in long-term PD patients. A mathematical model was established based on a classic compartment theory for clinical use. Model validations were made by comparing calculated results with clinical data in order to specify what was good about PD + HD combined therapy (5-day PD + 1-HD/week). Time-averaged concentration (TAC) for urea and creatinine decreased by 20% on the average by introducing PD+HD combined therapy no matter which dialyzers were used. TAC for β(2) -MG in PD+HD combined therapy, however, was strongly dependent upon the dialyzer clearance, and when a low flux dialyzer (clearance for β(2) -MG = 10 mL/min under Q(B) = 200, Q(D) = 500 mL/min) was used, pre-dialysis β(2) -MG concentration may increase. Use of super high-flux dialyzers (clearance for β(2) -MG = 60 mL/min under the same conditions) should greatly reduce the β(2) -MG concentration from 30 to 8 mg/L in 4-hr treatment. Then, when PD+HD combined therapy is introduced to a PD patient with diminishing RRF, use of super high-flux dialyzers may be strongly recommended in order not to increase concentrations of pre-dialysis β(2) -MG and/or even greater solutes. Use of super high-flux dialyzers is a key to the success of PD+HD combined therapy that could prevent concentrations of large solutes from increasing.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".