The Reality of Inadequate Patient Care and the Need for a Global Action Framework in Organ Donation and Transplantation
Bibliographic record
Abstract
BACKGROUND: Transplant therapy is considered the best and often the only available treatment for thousands of patients with organ failure that results from communicable and noncommunicable diseases. The number of annual organ transplants is insufficient for the worldwide need. METHODS: We elaborate the proceedings of the workshop entitled "The Role of Science in the Development of International Standards of Organ Donation and Transplantation," organized by the Pontifical Academy of Sciences and cosponsored by the World Health Organization in June 2021. RESULTS: We detail the urgency and importance of achieving national self-sufficiency in organ transplantation as a public health priority and an important contributor to reaching relevant targets of the United Nations Agenda for Sustainable Development. It details the elements of a global action framework intended for countries at every level of economic development to facilitate either the establishment or enhancement of transplant activity. It sets forth a proposed plan, by addressing the technical considerations for developing and optimizing organ transplantation from both deceased and living organ donors and the regulatory oversight of practices. CONCLUSIONS: This document can be used in governmental and policy circles as a call to action and as a checklist for actions needed to enable organ transplantation as treatment for organ failure.
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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 | 0.120 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.062 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.030 | 0.042 |
| Insufficient payload (model declined to judge) | 0.007 | 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".