Application of the 2017 KDIGO Guideline for the Evaluation and Care of Living Kidney Donors to Clinical Practice
Bibliographic record
Abstract
The Kidney Disease: Improving Global Outcomes (KDIGO) 2017 "Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors" was developed to assist medical professionals who evaluate living kidney donor candidates and provide care before, during, and after donation. This guideline Work Group concluded that a comprehensive approach to donor candidate risk assessment should replace eligibility decisions on the basis of assessments of single risk factors in isolation. To address all issues important to living donors in a pragmatic and comprehensive guideline, many of the guideline recommendations were on the basis of expert consensus opinion even when no direct evidence was available. To advance available evidence, original data analyses were also undertaken to produce a "proof-of-concept" risk projection model for kidney failure. This was done to illustrate how the community can advance a new quantitative framework of risk that considers each candidate's profile of demographic and health characteristics. A public review by stakeholders and subject matter experts as well as industry and professional organizations informed the final formulation of the guideline. This review highlights the guideline framework, key concepts, and recommendations, and uses five patient scenarios and 12 guideline statements to illustrate how the guideline can be applied to support living donor evaluation and care in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".