Implementing a home haemodialysis programme without adversely affecting a peritoneal dialysis programme
Bibliographic record
Abstract
BACKGROUND: As the population with stage 5 CKD grows, the associated costs of providing dialysis care increase. Due to the high costs of these therapies, home haemodialysis is enjoying a renaissance in many jurisdictions. However, concerns persist as to whether home haemodialysis programmes grow at the expense of other home therapies such as peritoneal dialysis. This study attempts to look at the impact of a new home haemodialysis programme on an existing peritoneal dialysis programme in the province of British Columbia. METHODS: Using the provincial renal database in British Columbia (PROMIS), all patients receiving dialysis were tracked over the years preceding the implementation of a home haemodialysis programme and following its implementation. Rate of growth by specific dialysis modality (hospital haemodialysis, community haemodialysis, home haemodialysis, and peritoneal dialysis) were tracked. RESULTS: When comparing the provincial growth rates in the peritoneal dialysis programme, using the 4 years before and following the introduction of the home haemodialysis programme, they were unchanged both annually (7.84% versus 7.34%) and overall (25.27% versus 23.62%). The growth within the home haemodialysis programme appears to have come from the community haemodialysis programme (annual growth rate 12.28% versus 5.87%) and in-hospital haemodialysis (annual growth rate 4.61% versus 1.3%). Incident rates of dialysis were similar both prior to and following the introduction of the home haemodialysis programme.Finally, only 6.4% of the total patients entering the home haemodialysis programme had discontinued peritoneal dialysis within the 6 months preceding home haemodialysis training, indicating a low frequency of movement from peritoneal dialysis to home haemodialysis. CONCLUSIONS: Successful implementation of a home haemodialysis programme can be done at a provincial level without having an adverse impact on the growth rate of existing peritoneal dialysis programmes.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".