OPTIMIZING SOLID ORGAN DONATION IN THE UNITED ARAB EMIRATES: LAUNCHING DONATION AFTER DEATH PROGRAMS
Bibliographic record
Abstract
Introduction: For the past few decades, living donors have been the sole source of solid organs In the UAE. Despite having good living donation rates, the solid organ demand kept increasing significantly. Transplant tourism became a phenomenon due to various reasons such as incompatibility or ethical restrictions to donation. In 2003, there were 23 kidney transplants the UAE, none of which was performed in the country (A Masri et al., 2004). A single center study conducted in Dubai showed that 45 pediatric patients travelled for organ transplantation between 1993 and 2009 (Majid, Al Khalidi, Ahmed, Opelz & Schaefer, 2010). To overcome these gaps and to expand the donor pool, deceased donation programs were established. By 2012, a total of 107 kidney transplants were performed in the country; 105 were from living donors while the other 2 were from deceased donors (donated by Eurotransplant) (Masri & Haberal, 2013). A legal framework for Donation after brain death (DBD) was created in 2017 and the deceased donation program was officially launched. (Al Obaidli et al., 2018) Methods: Quantitative study through the national committee of organ donation and transplantation from January 2017 to December 2019 and literature review. Results:Discussion: Self-sufficiency through deceased donation is the optimum option to prevent “commercial transplants” (Mohsin et al., 2014). “Transplantation is totally dependent on the supply of viable organs for implantation.” (McKeown, Bonser and Kellum, 2012). As the supply of viable organs was quite limited in the country, deceased donation programs were the most suitable solution. Following the lead of other middle-eastern countries like the Kingdom of Saudi Arabia and collaborating with the worlds’ leading country in organ donation, Spain. Studies prove that Brain dead donors are more likely to donate multiple organs thus, donation after brain death became the focus or the UAE’s deceased donor program. “quality of donor management is a major determinant of the outcome of DBD donation.” (McKeown, Bonser and Kellum, 2012). After the first deceased donor in 2017 the program had a linear progress with 21 donors by December 2019. Kidneys are the most transplanted organs, counting for up to 54%. 15 liver transplants were performed (20%). Conclusion: Despite limitations, the UAE deceased donation program has a promising rate of 3.5 within two years of starting. Brain dead donors are currently the sole deceased providers. Although quite beneficial, managing brain dead donors is challenging. Maintaining good deceased donation rates may be another challenge however, with the continuous efforts to educate citizens and health care professionals; as well as the solid support of the government, such a challenge is rather an opportunity. Adopting a DCD program may also lead to a steady increment in deceased donation rates although implementation may require some time. References: 1. Al Obaidli, A., Shaheen, F., Gómez, M., Procaccio, F., Quiralte, A., Revuelto, J., Vera, E. and Manyalich, M. (2018). First Series of Brain Death Organ Donors in United Arab Emirates - SEUSA Program Implementation. Transplantation, 102, p.S376. 2. Ambagtsheer, F., de Jong, J., Bramer, W. and Weimar, W. (2016). On Patients Who Purchase Organ Transplants Abroad. American Journal of Transplantation, 16(10), pp.2800-2815. 3. Kosieradzki, M., Jakubowska-Winecka, A., Feliksiak, M., Kawalec, I., Zawilinska, E., Danielewicz, R., Czerwinski, J., Malkowski, P. and Rowiński, W. (2014). Attitude of Healthcare Professionals: A Major Limiting Factor in Organ Donation from Brain-Dead Donors. Journal of Transplantation, 2014, pp.1-6. 4. Majid, A., Al Khalidi, L., Ahmed, B., Opelz, G. and Schaefer, F. (2010). Outcomes of kidney transplant tourism in children: a single center experience. Pediatric Nephrology, 25(1), pp.155-159. 5. Masri, M., AHaberal, M., Shaheen, F., J Ghods, A., Al-Rohani, M., Al Mousawi, M., Mohsin, N., Ben Abdallah, T., Bakr, A., Rizvi, A. and Stephan, A. (2004). Middle East Society for Organ Transplantation (MESOT) Transplant Registry. Experimental and Clinical Transplantation, 2(217-220). 6. Masri, M. and Haberal, M. (2013). Solid-Organ Transplant Activity in MESOT Countries. Experimental and Clinical Transplantation, 11(Supp 1), pp.1-8. 7. McKeown, D., Bonser, R. and Kellum, J. (2012). Management of the heartbeating brain-dead organ donor. British Journal of Anaesthesia, 108, pp.i96-i107. 8. Min, S., Ahn, C., Han, D., Kim, S., Chung, S., Lee, S., Kim, S., Kwon, O., Cho, H., Hwang, S., Kim, M., Yang, C., Ha, J. and Cho, W. (2015). To Achieve National Self-sufficiency. Transplantation, 99(4), pp.765-770. 9. Mohsin, N., Al-Busaidy, Q., Al-Marhuby, H., Al Lawati, J. and Daar, A. (2014). Deceased donor renal transplantation and the disruptive effect of commercial transplants: the experience of Oman. Indian Journal of Medical Ethics. 10. Oliver, M., Woywodt, A., Ahmed, A. and Saif, I. (2010). Organ donation, transplantation and religion. Nephrology Dialysis Transplantation, 26(2), pp.437-444. 11. Shemie, S. (2017). Trends in deceased organ donation in Canada. Canadian Medical Association Journal, 189(38), pp.E1204-E1205.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".