Satellite hemodialysis services for patients with end stage renal disease.
Bibliographic record
Abstract
More than 40,000 Canadians are living with end stage renal disease and approximately 22,400 of those are currently being treated with hemodialysis (The Kidney Foundation of Canada, 2013). Long distance travel to access hemodialysis services can be a serious burden for patients, and travelling more than 60 minutes can mean a 20% greater risk for death, as compared with those who travel 15 minutes or less (Moist et al., 2008). Satellite hemodialysis units are seen as one solution to this problem. This study assessed the impact of services provided by one satellite hemodialysis unit on patients' satisfaction, access to care and quality of life using a qualitative interview research design. Seven patients were interviewed and three themes emerged including the burden of long distance travel before satellite services (safety, time and cost), satisfaction with satellite services (pleasant environment and continuity of care), and improved quality of life. This study showed that a satellite hemodialysis unit improved access to services and enhanced the quality of life of those patients who participated in the study.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".