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Enregistrement W3081839597 · doi:10.1097/01.tp.0000699612.60322.ad

OPTIMIZING SOLID ORGAN DONATION IN THE UNITED ARAB EMIRATES: LAUNCHING DONATION AFTER DEATH PROGRAMS

2020· article· en· W3081839597 sur OpenAlexaboutno aff
Marwa S. Al Maskari, Manal S. Al Senaidi

Notice bibliographique

RevueTransplantation · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueOrgan Donation and Transplantation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDonationOrgan donationMedicineTransplantationKidney donationSurgeryFamily medicineKidney transplantationLawPolitical science

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,035
Tête enseignante GPT0,284
Écart entre enseignants0,248 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission1
Résumé présentoui

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