Has the UK living kidney donor population changed over time? A cross-sectional descriptive analysis of the UK living donor registry between 2006 and 2017
Bibliographic record
Abstract
BACKGROUND: A living-donor kidney transplant is the best treatment for most people with kidney failure. Population cohort studies have shown that lifetime living kidney donor risk is modified by sex, age, ethnicity, body mass index (BMI), comorbidity and relationship to the recipient. OBJECTIVES: We investigated whether the UK population of living kidney donors has changed over time, investigating changes in donor demographics. DESIGN: We undertook a cross-sectional analysis of the UK living kidney donor registry between January 2006 to December 2017. Data were available on living donor sex, age, ethnicity, BMI, hypertension and relationship to recipient. SETTING: UK living donor registry. PARTICIPANTS: 11 651 consecutive living kidney donors from January 2006 to December 2017. OUTCOME MEASURES: Living kidney donor demographic characteristics (sex, age, ethnicity, BMI and relationship to the transplant recipient) were compared across years of donation activity. Donor characteristics were also compared across different ethnic groups. RESULTS: Over the study period, the mean age of donors increased (from 45.8 to 48.7 years, p<0.001), but this change appears to have been limited to the White population of donors. Black donors were younger than White donors, and a greater proportion were siblings of their intended recipient and male. The proportion of non-genetically related non-partner donations increased over the 12-year period of analysis (p value for linear trend=0.002). CONCLUSIONS: The increasing age of white living kidney donors in the UK has implications for recipient and donor outcomes. Despite an increase in the number of black, Asian and minority ethnic individuals waitlisted for a kidney transplant, there has been no increase in the ethnic diversity of UK living kidney donors. Black donors in the UK may be at a much greater risk of developing kidney failure due to accumulated risks: whether these risks are being communicated needs to be investigated.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".