Adding Specialized Clinics for Remote-Dwellers with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study aimed to determine whether opening a new clinic in a remote region would be a cost-effective means of improving care for remote-dwellers with CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This study is a cost-utility analysis from a public payer's perspective over a lifetime horizon, using administrative data from a large cohort of adults with stage 3b-4 CKD in Alberta, Canada. The association between the distance from each simulated patient's residence and the practice location of the closest nephrologist and clinical outcomes (quality of care, hospitalization, dialysis, and death) were examined. A Markov 6-month cycle economic decision model was analyzed; estimates of the effect of a new clinic were based on the association between residence location, resource use, and outcomes. Costs are reported in 2009 Canadian dollars. RESULTS: The costs for equipping and operating a clinic for 321 remote-dwelling patients were estimated at $25,000 and $250,000/yr, respectively. The incremental cost-utility ratios (ICURs) ranged from $4000 to $8000/quality-adjusted life-year under most scenarios. However, if reducing distance to nephrologist care does not alter mortality or hospitalization among remote-dwellers, the cost-effectiveness becomes less attractive. All other one-way sensitivity analyses had negligible effects on the ICUR. CONCLUSIONS: Given the low costs of equipping and operating new clinics, and the very attractive ICUR relative to other currently funded interventions, establishing new clinics for remote-dwellers could play an important role in efficiently improving outcomes for patients with CKD. High-quality controlled studies are required to confirm this hypothesis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".