Attitudes and predictive factors for live kidney donation in British Columbia. A comparison of recipients and wait-list patients.
Bibliographic record
Abstract
INTRODUCTION: Live donor kidney transplantation (LDKT) is both medically and economically superior to cadaver kidney transplantation in the treatment of patients with chronic renal failure. Unfortunately, fewer than 50% of patients on the transplant waiting list have a relative or friend who contacts the transplant program about possible donation. We hypothesized that both the potential recipient and potential donor have identifiable and modifiable characteristics that contribute to the likelihood of a live donor transplant. MATERIALS AND METHODS: Specifically-designed and validated questionnaires addressing personal characteristics, knowledge and beliefs about LDKT were mailed to patients who had previously received a LDKT (N = 163) and patients on the cadaver transplant waiting list (N = 251). Response rates were 81% and 67% respectively. RESULTS: There were significant differences between groups in age, ethnicity, marital status, hours worked per week, annual income, and time on the waiting list. Significant differences were found between groups in both knowledge and beliefs about live donor kidney transplantation. All wait-list patients could identify at least one family member (mean = 7 potential donors per wait-list patient) who might serve as a live kidney donor but less than 13% of these potential donors have actually undergone an evaluation. CONCLUSIONS: In British Columbia, an enormous pool of potential live kidney donors exists for patients who are currently waiting for a cadaver kidney transplant. Educational strategies designed for wait-list patients may correct knowledge deficits and alter unfavorable beliefs about LDKT which, in turn, may increase their willingness to seek and accept an offer of live kidney donation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".