212.1: Knowledge and comfort gaps in the acceptance of kidneys from deceased donors: Results of a global survey.
Bibliographic record
Abstract
TTS Access to Transplantation working group and participating transplantation societies. Background: The Global Observatory on Donation and Transplantation (GODT) reported that <10% of the transplant (Tx) needs are met. We aimed to assess the knowledge and comfort gaps in the acceptance of kidneys from deceased donors (DD). Methods: We conducted a global survey from May 2022 to March 2023, involving nephrologists and kidney Tx surgeons. Responders were stratified according to the mean DD rate per million population (PMP) from 2016-2021 (2020 was excluded because of the COVID-19 pandemic): 1) No DD reported, or no data provided to GODT; 2) <10; 3) 10-19.9; 4) 20-29.9; 5) ≥30 DD PMP. We used ANOVA with a linear trend to compare the median gap (desired minus current) for knowledge or comfort. For example, I am aware of the potential benefits of kidney transplantation from expanded criteria donors (ECD); however, I feel uncomfortable accepting them. Results: Of 438 responders, 170 were kidney Tx specialists. We found differences in knowledge and comfort gaps across countries, mainly in those with low DD rates. Gaps in knowledge and comfort for the acceptance of kidneys from DD are depicted in Fig.1: ECD, KDPI >85%, determination of death by circulatory criteria, acute kidney injury, and Fig.2: Donors recovering from COVID-19 with negative PCR, diabetic donors, dual kidneys, and donors <10 yrs. Conclusion: The results of this survey suggest that there are global disparities in the acceptance of kidneys from DD. Educational activities should be considered to close the gaps in knowledge and comfort, with the goal of increasing access to kidney Tx worldwide.TTS Executive, TTS Council and Dr. Andrea Herrera-Gayol.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".