Combined Peritoneal Dialysis and Hemodialysis: Our Experience Compared to Others
Bibliographic record
Abstract
OBJECTIVE: To determine the clinical experience of using combined-modality [simultaneous hemodialysis (HD) and peritoneal dialysis (PD)] treatment in patients with end-stage renal disease. DESIGN: We reviewed data on 4 patients from our center that were treated with "combined-mode therapy." We then conducted a retrospective survey by sending questionnaires to nephrologists in the US and Canada by mail and by posting the survey on the Internet. Data queried included number of patients on combined modality, solute clearances, albumin levels pre and post combined therapy, reasons for using combined therapy, duration and success of combined therapy, and reimbursement issues. SETTING AND PARTICIPANTS: Ours is a tertiary-care center. Patients that were not doing well on PD alone were put on combined modality of treatment between 1992 and 1998. MAIN OUTCOME MEASURES: Clinical improvement in the indication for which the participant was started on combined modality. RESULTS: In response to the survey, data on 27 patients were collected. These data were combined with data on 4 patients from our unit that had previously been treated with combined HD and PD. Most patients were reported to have more than one clinical reason for changing from PD to combined therapy. The main clinical reason for offering combined treatments was inadequate solute clearance (34%), followed by ultrafiltration problems (16%) and neuropathy (11%). Mean duration of time followed on combined treatment was 8.5 +/- 0.12 months. Most patients tolerated combined treatment well and were reported to show improvement in the clinical reasons for which they needed the combined modality. Dual access and reimbursement issues were not a problem. There was no single method used for calculating total (HD, PD, and residual renal) solute clearance. No universal total solute clearance goal was reported. CONCLUSION: Hemodialysis and PD are not mutually exclusive. They can be used in combination to achieve targeted solute clearances, to improve certain clinical conditions, and to control volume and blood pressure in a subset of patients. Further evaluation is needed to better establish the long-term outcomes of using combined modality. Total solute clearance goals and methods for determining total solute clearance need to be standardized.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".