Effectiveness of first, second, and third COVID-19 vaccine doses in solid organ transplant recipients: A population-based cohort study from Canada
Bibliographic record
Abstract
Limited data exists on the effectiveness of a third COVID-19 vaccine dose in solid organ transplant recipients. We conducted a population-based cohort study using linked healthcare databases from Ontario, Canada to answer this question. We included solid organ transplant recipients (n = 12,842) as of December 14, 2020, with follow-up until November 28, 2021. We used an extended Cox proportional hazards model with vaccination status, including BNT162b2, mRNA-1273, and ChAdOx1 vaccines, modeled as a time-dependent exposure. Individuals started in the unvaccinated category (reference) and could contribute person-time to first, second, and third doses. Over a median follow-up of 349 days, 12.7% (n = 1632) remained unvaccinated, 54.1% (n = 6953) received 3 doses, and 488 (3.8%) tested positive for SARS-CoV-2 (of which 260 [53.3%] had a clinically important outcome [i.e., hospitalization or death]). Adjusted vaccine effectiveness against infection was 31% (95% CI: 2, 51%), 46% (95% CI: 21, 63%), and 72% (95% CI: 43, 86%) for one, two, and three doses. Vaccine effectiveness against clinically important outcomes was 38% (95% CI: 4, 61%), 54% (95% CI: 23, 73%), and 67% (95% CI: 11, 87%). Vaccine effectiveness in solid organ transplant recipients is lower than the general population, however, vaccine effectiveness improved following a third dose.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".