Latin America Transplantations Registry. Report 2015-2016
Bibliographic record
Abstract
The Transplantion Society of Latin America and the Caribbean (STALYC) is the association of transplant professionals for the development of transplantation in Latin America (LA) and to promote respecting the ethical and legal principies. Transplant reports in LA began in 1989, then they were developed as a Society activity, coordinated by V Duro-García and E Santiago-Delpín. LA has a population of 610.9 millons of inhabitants. Methods Data were colected from different sources, national registries, correspondants of each countrie, other registries. Results The first kidney transplant (KT) was performed in 1957, the lung (LT) in 1967 and the heart (HT), liver (LT) and pancreas (PT) in 1968. In total, 218,498 KT, 36,447 LT, 9,158 HT, 3,780 PT and 2,107 LT were performed in the region. The presumed consent for retrival in cadaveric donor (CD) exists in 13 countries and the informed in 7. The CD rate in the region went from 2.1 pmp in 2001 to 8.2 in 2016. In 2016, the rate ranged between 0.5 and 17.1 in the different countries. The transplant activity in 2016 was 11,973 KT (19.6 pmp), with 37% living donor (LD), 2,905 LT (4.8 pmp) with 8.7 from LD, 614 HT, 166 LT and 210 PT. The percentage of KT with CD was 63% and for LT of 8.7%. Two multivisceral transplantations were performed: 1 in Brazil and one in México. Conclusions The donor deceased rate is stabilized since 2012. All countries perform renal transplantation. The number of kidney transplants that was increasing, decreased in 2016. The rate of liver transplantation is annualy increasing and it was performed in 16 countries. The rate of heart transplantation that was increasing, stabilized in the last three years. The number of lung and pancreas transplants is small and performed in only a few countries. The small bowel and the multivisceral transplantation are not routinely performed in Latin America. The number of transplants in LA continues to grow, being the region with the highest growth in the last decade according to the Global Observatory of Donation and Transplantation. Despite this, the pmp number is lower than in the USA, Canada and Europe. There are strong differences between countries in the transplant rate. Some countries have not yet installed cadaveric or organ transplant programs. This points to the need to intensify and expand the transplant programs in the region to cover the treatment needs of the population.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".