Peritoneal Membrane Transport and Hypoalbuminemia: Cause or Effect?
Bibliographic record
Abstract
OBJECTIVE: Peritoneal membrane transport has been associated with serum albumin and clinical outcome. We examined the relationship between serum albumin and peritoneal membrane transport status before and after the initiation of peritoneal dialysis. SETTING: Patients were followed at a tertiary-care regional nephrology program at St. Joseph's Hospital, McMaster University, Hamilton, Ontario, Canada. METHODS: Incident peritoneal dialysis patients between 1 January 1995 and 31 May 1998 were eligible if there was a peritoneal equilibration test within 180 days of starting dialysis, and a serum albumin value measured within 90 days prior to, and 20 to 70 days after initiating dialysis. MAIN OUTCOME MEASURES: Serum albumin, before and after the initiation of dialysis, and the presence of proteinuric renal disease were compared with the peritoneal equilibration test results. RESULTS: Among 67 identified patients, there were 7 high, 27 high-average, 26 low-average, and 7 low transporters and the mean serum albumin values before dialysis were 35.1, 37.4, 37.8, and 40.4 g/L, respectively (p < 0.001). Serum albumin values prior to the initiation of dialysis correlated significantly with the 4-hour D/P creatinine ratio (r = -0.251, p = 0.040). After initiation of dialysis, the correlation was stronger (r= -0.447, p< 0.001). Serum albumin prior to initiation of dialysis was lower for those with proteinuric than nonproteinuric renal disease (36.4 g/L vs 38.8 g/L, p = 0.04). The trend to lower serum albumin in high transporters was seen in patients with both proteinuric and nonproteinuric renal disease. CONCLUSION: The association between higher peritoneal membrane transport and lower serum albumin is present before initiation of dialysis in both proteinuric and nonproteinuric renal disease. The poor outcomes associated with low serum albumin and higher peritoneal membrane transport might be explained by other underlying factors. The contribution of inflammation, malnutrition, and fluid overload requires further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".