Perspectives on Long-Term Follow-Up among Living Kidney Donors
Bibliographic record
Abstract
Key Points In a survey of 685 previous living kidney donors, donors wanted lifelong annual follow-up with a primary care provider. Living donors wanted information on clinical and laboratory assessment and health reassurance. Donors also wanted access to specialized care in the event of hospitalization or change in health. Background The long-term follow-up of living kidney donors is highly variable in Canada. Methods We surveyed perspectives on postdonation follow-up among 685 living donors in the two largest transplant programs in Canada (43% survey response rate). The anonymous survey was informed by semistructured interviews with 12 living kidney donors. The survey was developed on the basis of themes identified in the semistructured interviews, guidance from the research and clinical teams, and feedback from pilot testing with six previous donors. Results Most (73%) of the respondents received follow-up after the first donation year from a primary care provider, and 70% reported annual follow-up visits, including blood and urine tests. Most (71%) received a follow-up reminder from their transplant center, and follow-up was higher (86% versus 68%) among those receiving reminders. Donors wanted specialist involvement if new health or kidney-related events occurred. Most (70%) were satisfied with their follow-up, and 66% endorsed annual lifelong follow-up. Donors wanted more information about lifestyle and living donor outcomes and wanted to contribute to research to increase understanding of long-term donor health outcomes. Conclusions Donors wanted annual lifelong follow-up, including clinical assessment and laboratory tests, and more information about their postdonation health. A transplant center–led, primary care provider–administered model of long-term follow-up may best meet the care and information needs of most donors.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".