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Record W2407336551 · doi:10.20529/ijme.2014.041

Deceased donor renal transplantation and the disruptive effect of commercial transplants: the experience of Oman

2014· article· en· W2407336551 on OpenAlexaff
N. Mohsin, Q Al-Busaidy, H Al-Marhuby, Jawad Al‐Lawati, Abdallah S. Daar

Bibliographic record

VenueIndian Journal of Medical Ethics · 2014
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsPublic Health Ontario
FundersBrigham and Women's HospitalAmerican Society of Nephrology
KeywordsChristian ministryTransplantationMedicineRenal transplantEconomic growthPolitical scienceSurgeryLaw

Abstract

fetched live from OpenAlex

The Oman Renal Transplantation Program was established in 1988 as a joint venture between Sultan Qaboos University and the Ministry of Health. It began with both living related donor (LRD) and deceased donor (DD) transplants. Over the next nine years, while the LRD programme progressed relatively well, there were only thirteen DD transplants. Two of the DD kidneys were obtained from overseas via an active collaboration with the Euro-transplant organisation, and one DD kidney was obtained from Saudi Arabia within the Gulf Cooperative Council exchange programme. The rest of the DD kidneys were obtained in Oman. The Omani DD programme, although it was a pioneering effort in the Gulf region at the time, was not entirely sustainable. In this paper we focus on the challenges we encountered. Among the major challenges was the absence of resources to establish a dedicated DD programme and particularly the failure to develop a cadre of dedicated transplant coordinators.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.004
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.330
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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