Kidney Transplant Practices in Latin America During the COVID-19 Pandemic: Analysis from GlomCon Latin America Working Group (LGlomCon)
Bibliographic record
Abstract
Background: Latin America (LA) is the current epicenter of a global pandemic that has never been seen in the era of transplantation and immunotherapy. We aimed to describe their nephrologists’ practices and experiences regarding kidney transplant (KT) management in the context of COVID-19 pandemic. Methods: Descriptive analysis extracted from an online survey carried out among nephrologists, renal pathologists and other health workers treating kidney diseases between May 20-27, 2020 from sixteen Spanish speaking LA countries divided into 6 categories. We present the results for the kidney transplant category. Results: 430 responses were obtained of which 360 (84%) were considered for analysis. 139 (49%) respondents routinely participate in the care of transplant patients at centers that perform up to 50 KT per year (70% of them). The transplant activity was suspended in 90% of the centers at the time of the survey. Bigger centers continued their activity but not at full capacity. For transplant recipients who developed COVID-19, 52% of physicians continued the same maintenance immunosuppression for the ones with mild disease (outpatient). For moderate cases (inpatient but not requiring mechanical ventilation or vasopressors), 72% decreased maintenance therapy starting with the antiproliferative drug. For severe cases (ICU admission for mechanical ventilation or vasopressors), 74% stopped all the immunotherapy with the exception of steroids. ICU admission and need for renal replacement therapy were reported in less than 20% of their cases. Nephrologists from centers that continued their transplant activity during the pandemic reported that only 32% performed routine COVID-19 tests to donors and 51% to recipients before KT surgery. Conclusions: Kidney transplants programs are almost closed throughout LA during the COVID-19 pandemic. The disproportionate resource allocation to COVID-19 will have unintended consequences for those already carrying the burden of health inequality with the potential to disadvantage marginalized patients further. Reported immunosuppression management is in line with transplant societies’ recommendations. Funding: Private Foundation Support
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".