Comparison of COVID-19 outcomes in organ transplant recipients (OTr) and non-transplant patients: a study protocol for rapid review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has devastated the global community with nearly 4.9 million deaths as of October 2021. While organ transplant (OT) recipients (OTr) may be at increased risk for severe COVID-19 due to their chronic immunocompromised state, outcomes for OTr with COVID-19 remain disputed in the literature. This review will examine whether OTr with COVID-19 are at higher risk for severe illness and death than non-immunocompromised individuals. METHODS: MEDLINE (via Ovid and PubMed) and EMBASE (via Embase.com ) will be searched from December 2019 to October 2021 for observational studies (including cohort and case-control) that compare COVID-19 clinical outcomes in OTr to those in individuals without history of OT. The primary outcome of interest will be mortality as defined in each study, with possible further analyses of in-hospital mortality, 28 or 30-day mortality, and all-cause mortality versus mortality attributable to COVID-19. The secondary outcome of interest will be the severity of COVID-19 disease, most frequently defined as requiring intensive care unit admission or mechanical ventilation. Two reviewers will independently screen all abstracts and full-text articles. Potential conflicts will be resolved by a third reviewer and potentially discussion among all investigators. Methodological quality will be appraised using the Newcastle-Ottawa Scale. If data permit, we will perform random-effects meta-analysis with the Sidik-Jonkman estimator and the Hartung-Knapp adjustment for confidence intervals to estimate a summary measure of association between histories of transplant with each outcome. Potential sources of heterogeneity will be explored using meta-regression. Additional analyses will be conducted to explore the potential sources of heterogeneity (e.g., subgroup analysis) considering least minimal adjustment for confounders. DISCUSSION: This rapid review will assess the available evidence on whether OTr diagnosed with COVID-19 are at higher risk for severe illness and death compared to non-immunocompromised individuals. Such knowledge is clinically relevant and may impact risk stratification, allocation of organs and healthcare resources, and organ transplantation protocols during this, and future, pandemics. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework (OSF) registration DOI: https://doi.org/10.17605/osf.io/4n9d7 .
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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 | 0.055 | 0.066 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".