Regional effects of satellite haemodialysis units on renal replacement therapy in non-urban Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: To provide better dialysis care to rural communities, the Ministry of Health chose to build satellite haemodialysis (HD) units, which are affiliated with, but are distant to, a main renal centre. We considered whether constructing such units in rural regions of Ontario, Canada, alleviated under-service of rates of renal replacement therapy (RRT) locally, decreased patient travel distance and decreased local peritoneal dialysis (PD) utilization. METHODS: We compared two groups of rural regions at two time points (years 1995 and 2002) in a before and after cross-sectional study. These regions were either already serviced by a satellite unit in 1995 (control group, 10 communities), or had new satellite units built between the years 1995 and 2002 (exposure group, 24 communities). RESULTS: The exposure group had a slightly greater increase in prevalent rate of RRT over time, but this did not reach statistical significance (control group increased 401 per million, exposure group 436 per million, P = 0.8). The mean weekly travel distance was reduced by 210.6 km after the construction of new satellite units (P < 0.001). There was no significant difference between the groups in reduction of PD proportion (P = 0.4). There was a significant increase in the number of elderly receiving RRT once local access was provided. CONCLUSIONS: In conclusion, constructing satellite units increased access to renal care for elderly patients and reduced travel time for HD patients living in rural communities.
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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 | 0.002 |
| 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 | 0.000 |
| 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".