406.2: Global survey on system-level barriers that reduce access to transplantation.
Bibliographic record
Abstract
TTS Access to Transplantation working group and the participating transplantation societies. Introduction: The Global Observatory on Organ Donation and Transplantation (GODT) reported that <10% of the global transplantation (Tx) needs are met. Methods: From May 2022 to March 2023, a survey was conducted, including Tx physicians and surgeons. Responders were grouped as per the mean deceased donation (DD) rate per million population (PMP) from yrs 2016-2021 (excluding 2020 due to the COVID-19 pandemic) into: 1) No DD reported, or no data provided to GODT; 2) <10; 3) 10-19.9; 4) 20-29.9; and 5) ³ 30 DD PMP. Questions about hospital structure, legislative and financial barriers were answered by program directors (PD) as “yes”, “no”, or “unsure”. Fisher exact test was used for statistical analyses. Results: Significant answers from 150 PD out of 438 responders were the following: Fig.1.A. Hospital structure 1. Presence of local recovery teams and 2. Local Tx coordinators working in organ donation and Tx (ODT); 3. Strategies to increase the identification of DD, 4. Availability of training programs to manage brain-dead donors, and 5. Donors after circulatory determination of death (DCD); 6. Availability of critical care capacities to manage DD, and 7. Tissue typing laboratories. Fig.1.B. Legislation, policies, and allocation 1. Regulation of ODT activities, and 2. Brain death confirmation; 3. Policies about end-of-life care; 4. Regulatory mechanisms for donation from DCD and 5. Neurological criteria; 6. Policies on how to approach families and 7. Training counselors; 8. Need to create Tx programs, 9. ODT mandatory registries and 10. Allocation criteria for organ Tx. Fig 1.C. Financial barriers: 1. Access to Tx, 2. Treatment of post-Tx complications, 3. Adherence to immunosuppression, 4. Gender disparity in the access to Tx. In general, differences were mainly observed in countries with lower DD rates. Conclusions: The results of this survey suggest that there are system-level barriers that limit access to Tx. Tackling these challenges may contribute to increasing access to Tx globally.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".