A Call to Arms: Economic Barriers to Optimal Dialysis Care
Bibliographic record
Abstract
Epidemic growth rates and the enormous cost of dialysis pressure end-stage renal disease (ESRD) delivery systems around the world. Payers of dialysis services can constrain costs through (1) limiting access to dialysis, (2) reducing the quality of dialysis, and (3) placing constraints on modality distribution. In order to secure the necessary resources for ESRD care, we propose that the nephrology community consider the following suggestions: First, future leaders in dialysis should acquire additional advanced training in innovative pathways such as health care economics, business and health care administration, and health care policy. Second, the international nephrology community must strongly engage in ongoing advocacy for accessible, high quality, cost-effective care.Third, efforts should be made to better define and then implement optimal dialysis modality distributions that maximize patient outcomes but limit unnecessary costs. Fourth, industry should be encouraged to lower the unit cost of dialysis, allowing for improved access to dialysis, especially in developing countries. Fifth, research should be encouraged that seeks to identify measures that will reduce dialysis costs but will not impair quality of care. Finally, early referral of patients with progressive renal disease to nephrology clinics, empowerment of informed patient choice of dialysis modality, and proper and timely access creation should be encouraged and can be expected to help limit overall expenditures. Ongoing efforts in these areas by the nephrology community will be essential if we are to overcome the challenges of ESRD growth in this new decade.
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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 | 0.028 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.022 | 0.028 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".