Evolving Trends in Organ Donation and Transplantation Rates Across Muslim Majority Countries
Bibliographic record
Abstract
Muslim-majority countries differ in socio-cultural behavior and economic development but share a similar high burden of organ failure. Due to this heterogeneity, mapping organ donation and transplantation activity is of interest for future healthcare provision. Data was analyzed for 50 Muslim-majority countries (defined as Muslims comprising >50% of the population). Organ donation/transplantation rates were obtained from global registries between 2013-2023. Supplementary socio-economic and health data were obtained from open-source data repositories. Muslim-majority countries population increased from 1.53 billion to 1.88 billion between 2013-2023. Organ donation/transplant activity was only reported for 21/50 countries. Most organ donations came from living people rather than deceased donors (resulting in kidney and liver transplantation being the most common procedures). Other transplant activity rates were low. Poisson regression analyses identified multiple socioeconomic indicators to be associated with deceased- or living-donor activity, while negative binomial analyses comparing Muslim-majority to other countries within the region showed Muslim countries had lower deceased donation rates. Our study shows access to transplantation is lacking in many Muslim-majority countries. While socio-economic factors play a role, other challenges like religious and/or cultural barriers must be appreciated. With such global heterogeneity, bespoke country-specific interventions are warranted to improve transplantation opportunities in Muslim-majority countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".