Panic in the Pandemic: When Should Kidney Transplant Programs Close?
Bibliographic record
Abstract
BACKGROUND: Pandemics greatly interfere with overall health care delivery as resources are diverted to combat the crisis. Kidney transplantation programs were closed temporarily during the COVID-19 pandemic. Given the critical shortage of organs, their short shelf life, and their overall importance to improving length and quality of life for those with kidney disease, this analysis examines the impact of discarding deceased donor organs. METHODS: The net benefit (or harm) of discarding deceased donor organs was measured in projected life years from a societal and individual perspective using a Markov model. A wide range of infection rates, pandemic durations, and case fatality rates associated with infection in wait listed and transplant recipients were examined. RESULTS: Overall, patient life expectancy fell for both wait listed and transplant recipients as the pandemic conditions became more unfavorable. However, the overall net benefit of a transplant during the pandemic was preserved. For example, prior to the pandemic, the net benefit of a kidney transplant over dialysis was calculated to be 6.25 life years (LYs) or 8.24 quality-adjusted life years (QALYs) in a 40-year old recipient. This fell to 5.86 LYs (7.78 QALYs) during the pandemic. Even assuming plausible but higher relative case fatality rates and risks of nosocomial and donor transmission in transplant recipients compared to wait listed patients, the net benefit remained >4 years for most deceased donor organs. CONCLUSION: As long as hospitals have adequate resources to deal with the pandemic and can limit nosocomial infection, kidney transplantation should not be curtailed.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".