A Population-Based Analysis of the Impact of the COVID-19 Pandemic on Solid Organ Transplantation in Ontario, Canada
Bibliographic record
Abstract
Objectives: To evaluate the impact of the COVID-19 pandemic on solid organ transplantation. Background: COVID-19 caused unprecedented disruption to solid organ transplantation (kidney, liver, heart, lung). Concerns about safety and decreases in deceased donors due to pandemic lockdowns have been described as potential causes. Methods: We report population-based rates of transplantation during the first 3 waves of COVID-19 in Ontario, Canada (March 1, 2020-July 3, 2021) versus a pre-COVID-19 baseline period (January 1, 2017-February 29, 2020). Poisson models were used to predict transplantation rates during COVID-19, based on pre-COVID-19 rates, and generate observed to expected rate ratios (RRs). Ninety-day transplant outcomes (mortality, retransplantation, transplant nephrectomy) were captured. Results: A 34.4% decrease (RR, 0.656; 95% confidence interval [CI], 0.586-0.734) in transplant rates was observed, coinciding with wave 1 and the deployment of a provincial transplant triaging system. Transplants decreased by 14.6% in wave 2 (RR, 0.854; 95% CI, 0.770-0.947) and 23.1% in wave 3 (RR, 0.769; 95% CI, 0.690-0.857) despite the triaging system not being activated. Overall, there was a 24.3% decrease (RR, 0.757; 95% CI, 0.679-0.844) in transplant rates, equivalent to 409 fewer transplants. No sustained changes were observed in heart or liver but sustained and large decreases were seen for lung (RR, 0.664; 95% CI, 0.482-0.915) and kidney (RR, 0.721; 95% CI, 0.602-0.863) transplantation. A low prevalence (1.7%) of COVID-19 infection within 90 days of transplantation was seen. No differences were observed in other 90-day outcomes. Conclusions: Early safety concerns limited transplantation to immediate life-saving procedures; however, the reductions in kidney and lung transplants continued for the rest of the pandemic, where no restrictions were in place.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".