Peritoneal membrane characteristics in patients on peritoneal dialysis.
Bibliographic record
Abstract
Peritoneal dialysis (PD) is a well-established modality for treatment of patients with end-stage renal disease, giving excellent patient and technique survival rates. In Saudi Arabia, data collected by the Saudi Center for Organ Transplantation showed that in 2008, patients on PD accounted for a mere 4.8% of total patients on renal replacement therapy, including hemodialysis and renal transplantation. This study was conducted to identify the characteristics of membrane permeability in the Saudi population and to assess the role of various factors affecting solute transport across the peritoneal membrane. We followed up a total of 52 patients on Continuous Ambulatory Peritoneal Dialysis (CAPD) as well as Automated Peritoneal Dialysis (APD), being treated in the PD unit of the King Khalid University Hospital, Riyadh. There were 30 female and 22 male patients; 14 patients were using CAPD while 38 patients were on APD. The mean age of the patients was 50.5 years, with a range of 14-86 years. The average body mass index (BMI) was 27.1 kg/m² and the mean body surface area (BSA) of the study patients was 1.71 m². A standardized PET test was performed on all patients, 4-6 weeks after initiation of regular PD. The Kt/V and creatinine clearance measured 6-8 weeks after initiation of dialysis were 1.96 and 56.59 L/week, respectively. Residual renal function was assessed on the basis of daily urine output, using 24-hour urine collection. The mean serum urea con-centration was 16.91 mmol/L and mean serum creatinine was 702 μmol/L. According to the Peritoneal Equilibration Test (PET), 8% of the subjects belonged to the high trans-porter category, 44% patients belonged to the high-average transport group, 46% to the low-average category and 2% came in the low transporter category. Our study suggests that the patient characteristics and demographic para-meters seen in the Saudi population are comparable to those seen in other studies from the Middle East and worldwide, including data collected from Canada, New Zealand and Mexico.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".