Multiple factors affect 3‐year survival of patients on chronic hemodialysis
Bibliographic record
Abstract
Objective: The aim of this study is to determine the factors contributing to survival of patients on hemodialysis. Methods: Data were collected from 8 dialysis centers, and 432 patients were recruited. Among them, patients who underwent hemodialysis thrice a week for more than 3 years and received antihypertensive therapy or had systolic blood pressure of more than 160 mm Hg before dialysis secession were selected. Subsequent survival status and cause of death were ascertained for the next 3.5 years. The logistic multivariate regression analysis was used to estimate the relative risk of death. Variables used for the purpose of this analysis were patient gender, age, underlying renal disease (unknown and others (0), CGN (1), DM (2)), medication, serum albumin, history of having cerebrovascular and/or cardiovascular accidents (yes or no), the levels of serum creatinine, blood urea nitrogen, pre‐ and post‐dialysis blood pressure (systolic and diastolic), hemoglobin, serum calcium and inorganic phosphate, β2‐microglobulin, intact PTH, and other laboratory data. Results: First step logistic regression analysis indicated age, underlying renal diseases, serum creatinine, GOT, β2‐microglobulin were associated with increased relative risk (RR) of dying. According to the formula calculated in this analysis, if the levels of serum creatinine increased 1 mg/dl, the ratio of dying increased by 1.210. Also, compared to the patients aged between 50 to 64 years old, the patients aged between 65 to 90 years old had 1.521 times dying ratio. Conclusions: From this study, it is suggested that age, creatinine, underlying renal diseases, β2‐microblobulin, past history of cerebrovascular disease, serum creatinine, and GOT were important factors contributing to patients’ survival.
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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 | 0.002 |
| 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) | 0.002 | 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".