Engaging Living Kidney Donors in a New Paradigm of Postdonation Care
Bibliographic record
Abstract
Recent studies have highlighted the need for better understanding of the long-term health outcomes of living donors. Barriers to establishment of a dedicated long-term donor follow-up data system in the United States include infrastructure costs and donor retention. We propose providing all previous and future living donors with a lifelong health insurance benefit for the primary purpose of facilitating acquisition of health information after donation as an alternative to establishment of a dedicated donor follow-up data system. Donors would consent to allow collection and analysis of their medical data, and continuation of insurance coverage would require completion of regular health assessments. The extension of health insurance would be analogous to the established practice of paying people for participation in a research study and would provide a mechanism to engage donors in a new paradigm of postdonation care in which donors are actively involved in their own health maintenance. Rather than acting as an inducement for donation, providing donors with the ability to easily contribute information about their health status represents a practical strategy to acquire the long-term medical information necessary to better inform future generations of living kidney donors.
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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 | 0.129 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".