Evidence for a need to mandate kidney transplant living donor registries
Bibliographic record
Abstract
Kidney disease is a global public health problem of growing proportions. Currently the best treatment for end-stage renal failure is transplantation. Living organ donation remains a complex ethical, moral and medical issue. It is based on a premise that kidney donation is associated with short-term minimal risks to harm the donor, and is outweighed by the definite advantages to the recipient. A growing number of patients with end-stage renal disease and shortage of kidney donors poses a pressing need to expand the criteria needed to accept kidney donors. The current donor registries are structured and are driven to expand donor pool. As living kidney donation is not without risks, more attention should be given to protect the donor health. After kidney donation, mild to moderate renal insufficiency may occur. Renal insufficiency, even mild, is associated with increased risks of hypertension, proteinuria and cardiovascular morbidity. We, therefore, foresee a need to mandate the establishment of renal transplant donor registries at all transplanting programs as a prerequisite to protect the long-term well being of kidney donors. These registries can collect the database necessary to develop standards of practice and guidelines for future 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 | 0.292 | 0.482 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".