Effect of COVID-19 on Kidney Transplantation Across the Americas
Bibliographic record
Abstract
Background: Kidney transplant (KT) is the preferred modality for RRT, offering better long-term outcomes and quality of life. The COVID-19 pandemic was a catastrophic event that affected healthcare systems worldwide. Its impact on kidney transplantation in the Americas, including Latin America, is understudied. In this study, we evaluated the effects of the COVID-19 pandemic on kidney transplant systems across the Americas Methods: KT data were obtained from the GODT transplant repository, a collaborative initiative between the WHO and the Spanish National Transplant Organization. We collected data from 2018 to 2023 to determine the trends of KT. Results: Significant heterogeneity in the effects of the COVID-19 pandemic on KT practices is observed across the Americas. The US and Canada experienced less disruption, although there was a decline in 2020, a notable increase was seen from 2021 to 2023, with KT volumes surpassing pre-pandemic levels. In Latin America, Brazil is relatively unaffected compared to other countries in the region. While Mexico, Colombia, Uruguay, and Argentina were impacted, they recovered faster, showing trends towards pre-pandemic numbers. Other countries, such as Costa Rica, Panama, Dominican Republic, Ecuador, and Peru, took two years to recover and are now at pre-pandemic numbers. Interestingly, Guatemala faced challenges but reached a similar level to those before the pandemic by 2023. Unfortunately, Trinidad and Tobago and Cuba, were significantly affected, with a slow recovery process. Conclusion: The impact of the COVID-19 pandemic on organ donation systems was significant in most countries in the Americas. Kidney transplantation numbers in Central and South America were affected for multiple years even after the pandemic. Further investigation into country-specific challenges is neeeded to develop strategies to improve access to and outcomes of KT.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".