Associations with home hemodialysis modality failure and mortality
Bibliographic record
Abstract
BACKGROUND: Limited data exist on risk factors for home hemodialysis (HH) failure and mortality. We sought to determine whether age, helper status, or ethnicity was associated with home dialysis failure or mortality. METHODS: We conducted a retrospective cohort study of all prevalent and incident patients from a regional dialysis unit who initiated HH training from December 2000 to September 2002. Baseline demographics, program entry and exit dates, and mortality were ascertained. Characteristics of those more likely to remain in the program were assessed using logistic regression; survival was determined using Cox proportional hazards models. RESULTS: Of the 1117 patients enrolled for dialysis, 116 patients were trained in the HH program (6.8%). Of those, 45.7% remained in the program, 10.3% received a transplant, 10.3% returned to in-center dialysis, 1.7% were lost to follow-up, and 31.7% expired. Compared to patients who returned to center or received a transplant, patients who remained on HH were more likely to be older, to have been on dialysis longer, and to have diabetes as their primary renal disease. Ethnicity, sex, or type of helper did not affect home program status. Among those who remained in the HH program, those with hypertension or other renal diseases had better survival than those with diabetes, as did those who had related helpers compared to those with unrelated helpers. CONCLUSIONS: Older and younger ages, but not ethnicity, helper status, or sex, were associated with home dialysis failure. Diabetes remained an independent risk factor for increased mortality. HH remains a viable option for many 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 | 0.000 | 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 | 0.000 |
| 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".