Barriers to living kidney donation identified by eligible candidates with end-stage renal disease
Bibliographic record
Abstract
BACKGROUND: Among eligible transplant candidates with end-stage renal disease, only a minority receive a living donor kidney transplant (LDKT), suggesting that there are barriers to receipt of this optimal therapy. METHODS: A validated questionnaire was administered to adults active on the deceased donor transplant waiting list, identified from the Southern Alberta Renal Program database. The questionnaire included both quantitative and qualitative items addressing issues related to LDKT in the categories of knowledge, opportunity, fear and guilt. RESULTS: Of the 196 subjects invited to complete the questionnaire, 145 (74%) responded. Not knowing how to ask someone for their kidney was the most frequently reported barrier, identified by 71% of respondents. Those that stated that living donation did not pose significant long-term health risks to the donor [odds ratio (OR)=3.40, 95% CI 1.17-9.46, P=0.01] and those who understood how and why to begin the living donation process (OR=4.21, 95% CI 1.41-12.04, P=0.002) were more likely to have discussed living donation with potential donors. CONCLUSIONS: Knowledge about living donation was associated with having discussed living donation with a family member or friend. Studies examining the impact of educational programmes which address these barriers to living donor kidney transplantation are required.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".