What is the Optimal Frequency of Cycling in Automated Peritoneal Dialysis?
Bibliographic record
Abstract
OBJECTIVE: The recent increase in the use of automated peritoneal dialysis (APD) has led to concerns about the adequacy of clearances delivered by this modality. Few clinical studies looking at the effects of varying the individual components of the APD prescription on delivered clearance have been done, and most published data are derived from computer modeling. Most controversial is the optimal frequency of exchanges per APD session. Many centers prescribe 4 to 6 cycles per night but it is unclear if this is optimal. The purpose of this study was to address at what point the beneficial effect of more frequent cycles is outweighed by the concomitant increase in the proportion of the total cycling time spent draining and filling. METHODS: A comparison was made between the urea and creatinine clearances (CCrs) achieved by 4 different APD prescriptions, used for 7 days each, in 18 patients. The prescriptions were for 9 hours each and were all based on 2-L dwell volumes, but differed in the frequency of exchanges. They were 5 x 2 L, 7 x 2 L, and 9 x 2 L, as well as a 50% tidal peritoneal dialysis (TPD) prescription using 14 L. Ultrafiltration, dwell time, glucose absorption, sodium and potassium removal, protein excretion, and relative cost were also compared. Clearances due to day dwells and residual renal function were not included in the calculation. RESULTS: Mean urea clearances were 7.5, 8.6, 9.1, and 8.3 L/night for the four prescriptions respectively. Urea clearance with 9 x 2 L was significantly greater than with the other three prescriptions (p < 0 0.05). Urea clearance with 7 x 2 L and TPD were superior to 5 x 2 L (p < 0.05). Mean CCr was 5.1, 6.1, 6.4, and 5.6 L/night, respectively. Compared to 5 x 2-L, the 7 x 2-L, 9 x 2-L, and TPD prescriptions achieved greater CCr (p < 0.05). Taking both urea and CCr into account, 9 x 2 L was the optimal prescription in 12 of the 18 patients. Ultrafiltration and sodium and potassium removals were all significantly greater with the higher frequency prescriptions. CONCLUSION: The 5 x 2-L prescription significantly underutilizes the potential of APD to deliver high clearances, and 7 x 2 L is a consistently superior prescription if 2-L dwells are being used. Although more costly, 9 x 2 L should be considered if higher clearances are required.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".