COMPARING COUNTRIES’ PERFORMANCE IN ORGAN DONATION: TIME TO FOCUS ON THEIR REAL POTENTIAL
Bibliographic record
Abstract
O467 Aims: Donation rates from deceased donors have been traditionally calculated as ’donors per million population (pmp)’. This international standard for comparing countries’ donation performance does not take into account individual countries’ potential for donation. We have calculated a ’Donation Efficiency Index’ (DEI) for 26 countries with active transplant programs in Europe, North America and Australasia, as a more accurate estimate of how these countries convert their theoretical potential for donation into actual donors. Methods: WHO Registered Deaths statistics (1999-2000) were used to calculate cumulative pmp death rates for ICD9 or ICD10 coded selected causes (cerebrovascular accidents (CVA), road traffic accidents (RTA), falls, other accidents and homicides), and for patients below the age of 75. In 2000, these causes accounted for 86% and 96% of all deceased donors reported to UNOS and Eurotransplant respectively. Countries’ DEI was calculated as the % of donors in 2000 to the cumulative death rates for selected causes. Results: Cumulative death rates for selected causes pmp as a measure of potential for donation were highest in Latvia (2235), Romania (1663) and Hungary (1234), and lowest in Australia (377), Canada (351) and The Netherlands (335). Of all death causes, RTA accounted for 21.8% on average (highest in New Zealand (35.2%), Spain (31%) and the USA (30.9%), and lowest in Finland (12.9%), Hungary (11.8%) and Romania (9.4%)). CVA accounted for 51.4% on average (highest in Romania (71%), Portugal (70.8%), and Hungary (67.4%), and lowest in France (36.5%), the USA (31.9%) and Switzerland (26%)). DEI was highest in Spain (5.9%), Austria (4.9%), Belgium (4.8%), Norway (4.6%) and Canada (4.4%), and lowest in Poland (1.2%), Hungary (1.1%), Latvia (0.8%), Greece (0.2%) and Romania (0.1%). The largest differences between the traditional ’donors pmp’ and ’DEI’ ratings were observed in Canada (15.4 pmp vs. 4.4%), The Netherlands (12.6 pmp vs. 3.8%) and the U.K. (13.1 pmp vs. 3.3%). Conclusions: While the DEI approach may not exclude patients dying outside the ICU and/or being unsuitable for organ donation, this method allows for calculating performance rates more accurately and on a population with some immediate potential for becoming a donor, rather than on a country’s total population. The method also allows for taking into account changes in mortality patterns over the years when comparing countries’ donation efficiency.
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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 | 0.000 | 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 | 0.001 |
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 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".