Prevalence and Predictive Value of Hypoalbuminemia in Appalachians With Chronic Kidney Disease
Bibliographic record
Abstract
Background : Chronic kidney disease (CKD) has reached epidemic proportions worldwide and in the United States, racial minorities reach end stage renal disease (ESRD) at a disproportionate rate. A rural state in the heart of Appalachia, West Virginia leads the nation in rates of incident ESRD, despite its predominantly Caucasian population. Characteristics of this racially homogeneous CKD population are herein examined for their impact on progression to ESRD or death. Methods : Retrospective analysis of demographic and clinical information for 4258 patients seen between 2001 and 2010. Associations between risk factors and outcomes were assessed for significance using Cox Proportional Hazards models. Results : Patients with CKD were largely Caucasian (94.3%), 39% diabetic, with a mean age of 60.1 ± 16.7; 39% presented with serum albumin levels ? 3.5 g/dl. Patients with higher albumin levels had better survival and less progression to dialysis than those with lower levels (P < 0.0001). Hypoalbuminemia, hypocalcemia, hyperparathyroidism and anemia independently correlated with reduced survival and more rapid progression to ESRD (P < 0.0001). Compared to those from more affluent counties, patients from poorer counties had lower albumin levels (3.36 ± 0.014 vs 3.68 ± 0.079 gm/dl; P < 0.04) and higher rates of progression to dialysis or death (P < 0.016). Conclusions : In this predominantly Caucasian population of central Appalachia, hypoalbuminemia and residence in a county of low socioeconomic status independently predicted overall survival and progression to dialysis, suggesting that poverty and culture, irrespective of race, warrant further study for their impact on outcomes in patients with CKD. doi:10.4021/wjnu3e
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".