Determinants of training and technique failure in home hemodialysis
Bibliographic record
Abstract
Home hemodialysis (HHD) has clinical and economic advantages compared with in-center conventional hemodialysis. Many health systems wish to broaden the population to which this modality can be successfully offered. However, determinants of successful HHD training and technique survival are unknown. We hypothesize that both medical and social factors play a role when patients fail to successfully adopt HHD. We examined characteristics of consecutive patients who initiated training for HHD between 2003 and 2011. Patients were classified as "failure" if they failed to complete HHD training or experienced technique failure (TF) within the first year of treatment. Remaining patients were classified as "success." One hundred seventy-seven patients initiated HHD training. In the "failure" group (n = 32), 24 did not finish training and 8 had TF. In the "success" group (n = 145), 65 (45%) patients remained on NHD, 49 (34%) discontinued HHD because of renal transplantation and 21 (14%) because of death, while only 10 (7%) eventually transferred to another dialysis modality. In a multivariable logistic regression analysis, the strongest predictors of "failure" were end-stage renal disease because of diabetes (odds ratio [OR] 3.8, 95% confidence interval [CI] 1.4-10.3, P = 0.008) and use of rental housing (OR 3.1, 95% CI 1.3-6.0, P = 0.01). Both medical and social factors are associated with failure to adopt HHD. Enhanced supports or a customized education strategy for these vulnerable patients should be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".