Institutional factors influencing regional differences in the 1‐year survival of dialysis patients
Bibliographic record
Abstract
There are regional differences in the survival of incident dialysis patients, but few studies have investigated the reasons. We assessed the effect of institutional factors on factors on survival (by both cross-sectional assessment and after a 1-year investigation) in a cohort of the Japanese Society for Dialysis Therapy (JSDT). We investigated 20 institutional factors from 3958 dialysis institution data of the 47 prefectures in Japan in 2005 and the JSDT database of 102,011 patients who commenced dialysis during 2004-2006. Univariate regression analysis between 20 factors and 1-year survival rate, Kaplan-Meier method, log-rank test, and Cox's proportional hazard model between the upper 24 and the lower 23 prefectures of the significant factors were carried out. The age-adjusted 1-year survival rate was 0.832 ± 0.027. Deaths occurred in 15.0% in 24 upper survival prefectures and 18.7% in 23 lower survival prefectures (P < 0.0001, unadjusted hazard ratio [HR] of death in lower survival prefectures: 1.26, 95% confidence interval [CI]: 1.17-1.40). A total of five factors among males were significantly correlated with 1-year survival according to the univariate regression analysis. Among them, three factors (night-time center/total dialysis center ratio [males: P < 0.0001, age, genders, and presence of diabetes adjusted HR: 0.88, 95% CI: 0.81-0.93], number of full-time dialysis nurses [males: P = 0.0427, 0.94, 0.87-1.00], number of full-time dialysis dietitians [males: P = 0.0084, 0.92, 0.85-0.98], respectively) were significant in Kaplan-Meier analysis, log-rank test, and the Cox's model. Institutional factors were closely related to the survival of incident dialysis patients, and regional differences in the survival may be explained, at least partly, by these factors.
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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 | 0.002 |
| 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 | 0.000 | 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".