Long-term Care of Living Kidney Donors Needs a Better Model of Healthcare Delivery
Bibliographic record
Abstract
Every year, over 30,000 healthy individuals globally donate a kidney to a patient with kidney failure. These living kidney donors are at higher risk of some medical complications post-donation when compared with matched controls. Although the absolute risk of these complications is low, appropriate long-term care is essential to allow early detection and timely interventions. Some transplant centers follow living donors long-term, but many recommend that donors regularly see a primary care practitioner post-donation. However, primary care is currently not integrated with transplant centers, and the two often work in silos with little to no channels of communication with each other. As this model of care is suboptimal, existing evidence suggests that post-donation care and follow-up are inadequate. We argue for an integrated model of living donor care with stronger continuity and coordination between primary care and transplant centers that are developed with the input of all relevant stakeholders.
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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| 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".