Reasons for Home Hemodialysis Discontinuation in a Canadian Province
Bibliographic record
Abstract
Background: Home hemodialysis (HHD) utilization is greater than 8% in Manitoba, Canada. Despite this, many patients stop HHD within the first several years. The present study aims to identify local rates of HHD discontinuation and the associated reasons, including potentially modifiable causes of technique failure. Methods: The target population includes adults in Manitoba, Canada with kidney failure who began HHD between January 1st, 2017 and December 31st, 2023. Data was collected retrospectively from an electronic health record and chart review. HHD discontinuation was defined as stopping for greater than sixty days. Results: During the study period, 266 patients completed HHD training. In total, 142 patients discontinued HHD with a mean time of 751 days (range 24 to 2225 days). The rates of discontinuation and technique failure at 12 and 36 months are reported in Table 1. A qualitative description of the psychosocial reasons for technique failure are outlined in Figure 1. Conclusion: Rates of HHD discontinuation in Manitoba, Canada are similar to previous published administrative database and program reviews. Our findings should encourage leaders in HHD to standardize technique failure as a key performance indicator, develop local initiatives to support patients with modifiable psychosocial reasons for technique failure, and encourage providers to discuss end of life goals of care when stopping HHD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".