A Population-Based Analysis of the Impact of the COVID-19 Pandemic on Solid Organ Transplantation in Ontario, Canada
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it