Follow-up Care of Living Kidney Donors in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Previous guidelines recommend that living kidney donors receive lifelong annual follow-up care to assess renal health. OBJECTIVE: To determine whether these best practice recommendations are currently being followed. DESIGN: Retrospective cohort study using linked health care databases. SETTING: Alberta, Canada (2002-2014). PATIENTS: Living kidney donors. MEASUREMENTS: We determined the proportion of donors who had annual outpatient physician visits and laboratory measurements for serum creatinine and albuminuria. RESULTS: There were 534 living kidney donors with a median follow-up of 7 years (maximum 13 years). The median age at the time of donation was 41 years and 62% were women. Overall, 25% of donors had all 3 markers of care (physician visit, serum creatinine, albuminuria measurement) in each year of follow-up. Adherence to physician visits was higher than serum creatinine or albuminuria measurements (67% vs 31% vs 28% of donors, respectively). Donors with guideline-concordant care were more likely to be older, reside closer to the transplant center, and receive their nephrectomy in more recent years. LIMITATIONS: Our results may not be generalizable to other countries that do not have a similar universal health care system. CONCLUSIONS: These findings suggest significant evidence-practice gaps, in that the majority of donors saw a physician, but the minority had measurements of kidney function or albuminuria. Future interventions should target improving follow-up care for all 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".