Need for a Registry of Living Kidney Donor Outcomes
Bibliographic record
Abstract
We read with interest the proposal by Tan et al. (1) for a live donor nephrectomy complications classification scheme, which they suggest be used as a template for the development of a national or international registry of donor outcomes. With the increasing dependence upon live donors as a source of organs for patients with end-stage renal disease, this initiative is both timely and imperative. The short-term complications of kidney donation including intraoperative and early postoperative adverse events have been relatively well-documented, and form the majority of data for collection in the proposed scheme (1). As correctly pointed out, these data are critical both for setting quality benchmarks and evaluating new techniques such as laparoscopic nephrectomy. For obvious reasons, these data are relatively simple to gather because of the existence of a relationship between the surgical provider and the donor when they occur. The long-term complications of kidney donation are, however, less clear; data are much more difficult to collect since the relationship between the transplant service provider and the donor is often lost. These potential complications are underrepresented in the proposed scheme (1). Any new registry that is developed should also include, at minimum, a provision for the collection of information on all-cause mortality, cardiovascular disease, blood pressure change and the development of hypertension, level of renal function and the occurrence of chronic kidney disease, and development of proteinuria. This information may be collected even without the occurrence of an obvious intra- or postoperative complication, emphasized in the scheme proposed by Tan et al. Other items of interest might include the development of diabetes mellitus, as well as psychosocial and economic implications of donation (2). Counseling of potential kidney donors is currently limited by the availability of predominantly retrospective studies using nonideal controls with incomplete follow-up (3) along with lack of information on the true occurrence rate of these long-term events, and therefore requires the collection of data similar in quality to that of surgical complication rates. If we are to take donor nephrectomy outcomes forward, it is important that in a future national or international living kidney donor registry the collection of information on long-term medical complications of donation receives equal emphasis. However, the cost and sustainability—as well as methodological, legal, and ethical considerations—of such an initiative remain a challenge, and require due creative solutions. G. V. Ramesh Prasad Division of Nephrology St. Michael's Hospital University of Toronto Toronto, Ontario, Canada Amit X. Garg Division of Nephrology London Health Sciences Centre University of Western Ontario London, Ontario, Canada
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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 | 0.083 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".