Predictors of Loss of Residual Renal Function among New Dialysis Patients
Bibliographic record
Abstract
Residual renal function (RRF) in end-stage renal disease is clinically important as it contributes to adequacy of dialysis, quality of life, and mortality. This study was conducted to determine the predictors of RRF loss in a national random sample of patients initiating hemodialysis and peritoneal dialysis. The study controlled for baseline variables and included major predictors. The end point was loss of RRF, defined as a urine volume <200 ml/24 h at approximately 1 yr of follow-up. The adjusted odds ratios (AOR) and P values associated with each of the demographic, clinical, laboratory, and treatment parameters were estimated using an "adjusted" univariate analysis. Significant variables (P < 0.05) were included in a multivariate logistic regression model. Predictors of RRF loss were female gender (AOR = 1.45; P < 0.001), non-white race (AOR = 1.57; P = <0.001), prior history of diabetes (AOR = 1.82; P = 0.006), prior history of congestive heart failure (AOR = 1.32; P = 0.03), and time to follow-up (AOR = 1.06 per month; P = 0.03). Patients treated with peritoneal dialysis had a 65% lower risk of RRF loss than those on hemodialysis (AOR = 0.35; P < 0.001). Higher serum calcium (AOR = 0.81 per mg/dl; P = 0.05), use of an angiotensin-converting enzyme inhibitor (AOR = 0.68; P < 0.001). and use of a calcium channel blocker (AOR = 0.77; P = 0.01) were independently associated with decreased risk of RRF loss. The observations of demographic groups at risk and potentially modifiable factors and therapies have generated testable hypotheses regarding therapies that may preserve RRF among end-stage renal disease patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".