Model for Equitable Care and Outcomes for Remote Full Care Hemodialysis Units
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Remotely located patients not living close to a nephrologist present major challenges for providing care. Various models of remotely delivered care have been developed, with a gap in knowledge regarding the outcomes of these heterogeneous models. This report describes a satellite care model for remote full-care hemodialysis units managed homogenously in the province of Manitoba, Canada, without onsite nephrologists. Survival in remotely located full-care units is compared with a large, urban full-care center with onsite nephrologists. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data from a Canadian provincial dialysis registry were extracted on 2663 patients between 1990 and 2005. All-cause mortality after initiation of chronic hemodialysis was assessed with Cox proportional hazards regression. Both short-term (1 year) and long-term (2 to 5 years) survival were analyzed. RESULTS: Survival for patients receiving remotely delivered care was shown to be better than for those receiving care in the urban care center with this particular Canadian model of care. Furthermore, there was no difference when assessing short- and long-term survival. This was independent of distance from the urban center. CONCLUSIONS: Chronic hemodialysis patients receiving remotely delivered care in a specialized facility attain comparable, if not better survival outcomes than their urban counterparts with direct onsite nephrology care. This model can potentially be adapted to other underserviced areas, including increasingly larger urban centers.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".