Association of increased travel distance to dialysis units with the risk of anemia in rural chronic hemodialysis elderly
Bibliographic record
Abstract
Geographic remoteness has been found to influence health-related outcomes negatively. As reported in the literature, rural dialysis patients have a higher risk of mortality with increasing travel distance to dialysis units. However, few studies have focused on the impact of travel distances on the development of dialysis complications. We utilized a prospectively collected chronic hemodialysis patient cohort from a rural regional hospital for analysis. Data on demographics, comorbidities, and serum laboratory results were obtained. Correlation analyses between travel distance to dialysis units and dialysis complications were conducted, and significantly correlated parameters were entered into multivariate logistic regression models to determine their exact associations. A total of 46 rural chronic hemodialysis patients were enrolled, with an average age higher than others in the literature. Significant correlation was found between travel distance and serum hemoglobin levels (R(2) = -0.34, P value = 0.029). Multivariate logistic regression found that every 1 km increase in travel distance was associated with an increased risk of anemia (hemoglobin <9 g/dL) (odds ratio 1.46; P value = 0.01). Sensitivity analyses further showed that the associated risk was partially attenuated by serum albumin (odds ratio 1.83; P value = 0.07) and ferritin (odds ratio 1.39; P value = 0.08) levels. This is the first study to demonstrate the association between increased travel distance to dialysis units and the risk of anemia in chronic dialysis patients, especially elderly. Malnutrition, inflammation, and atherosclerosis syndrome could be partially responsible for the observed association. Further research is required to confirm our findings.
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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 | 0.001 |
| 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 | 0.000 |
| 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".