218.3: Global survey on disparities in education on organ donation and transplantation.
Bibliographic record
Abstract
TTS Access to Transplantation working group and the participating transplantation societies. Introduction: The Global Observatory on Donation and Transplantation (GODT) reported that <10% of the transplantation (Tx) needs are met. Our goal was to evaluate education on organ donation and transplantation (ODT) globally. Methods: We performed a global survey from May 2022 to March 2023, involving Tx physicians and surgeons. The answers were sorted as per the mean deceased donation (DD) rate per million population (PMP) from yrs 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. Results: We analyzed 438 answers. We found significant differences across countries according to the DD rate on education on ODT in the following: Fig.1.A.1. postgraduate medical education in your country (P=0.003) and 1.A.2. region (P=0.03); Fig.1.B.1. undergraduate medical school in your country (P<0.001) and 1.B.2. region (P=0.04); Fig.1.C.1. in elementary school in your country (P=0.007) and 1.C.2. region (P=0.04); Fig.1.D.1. in high school in your country (P=0.002) and Fig.1.D.2. region (P=0.006); and Fig.1.E.1. for the public in your country (P<0.001), and Fig.1.E.2. region (P<0.001). The differences were observed mainly in counties with lower DD rates. Conclusion: This survey suggests that there are differences in education on ODT globally, ranging from elementary school to public and postgraduate medical studies. Increased needs were seen in countries with decreased DD rates. Educational activities that focus on national and regional needs should contribute to increasing knowledge about ODT and access to Tx worldwide.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".