Long-Term Outcomes for Living Kidney Donors With Early Guideline-Concordant Follow-up Care: A Retrospective Cohort Study
Bibliographic record
Abstract
Background:Current guidelines recommend that living kidney donors receive lifelong annual follow-up care to monitor kidney health. In the United States, the reporting of complete clinical and laboratory data for kidney donors has been mandated for the first 2 years post-donation; however, the long-term impact of early guideline-concordant care remains unclear.Objective:The primary objective of this study was to compare long-term post-donation follow-up care and clinical outcomes of living kidney donors with and without early guideline-concordant follow-up care.Design:Retrospective, population-based cohort study.Setting:Linked health care databases were used to identify kidney donors in Alberta, Canada.Patients:Four hundred sixty living kidney donors who underwent nephrectomy between 2002 and 2013.Measurements:The primary outcome was continued annual follow-up at 5 and 10 years (adjusted odds ratio with 95% confidence interval, LCLaORUCL). Secondary outcomes included mean change in estimated glomerular filtration rate (eGFR) over time and rates of all-cause hospitalization.Methods:We compared long-term follow-up and clinical outcomes for donors with and without early guideline-concordant care, defined as annual physician visit and serum creatinine and albuminuria measurement for the first 2 years post-donation.Results:Of the 460 donors included in this study, 187 (41%) had clinical and laboratory evidence of guideline-concordant follow-up care throughout the first 2 years post-donation. The odds of receiving annual follow-up for donors without early guideline-concordant care were 76% lower at 5 years (aOR 0.180.240.32) and 68% lower at 10 years (aOR 0.230.320.46) compared with donors with early care. The odds of continuing follow-up remained stable over time for both groups. Early guideline-concordant follow-up care did not appear to substantially influence eGFR or hospitalization rates over the longer term.Limitations:We were unable to confirm whether the lack of physician visits or laboratory data in certain donors was due to physician or patient decisions.Conclusions:Although policies directed toward improving early donor follow-up may encourage continued follow-up, additional strategies may be necessary to mitigate long-term donor risks.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".