Viral cultures, Polymerase Chain Reaction Cycle Threshold Values and Viral Load Estimation for SARS-CoV-2 Infectious Potential Assessment in Hematopoietic Stem Cell and Solid Organ Transplant Patients: A Systematic Review
Bibliographic record
Abstract
Abstract Background Organ transplant recipients are at increased vulnerability to SARS-CoV-2 due to immunosuppression and may pose a continued transmission risk especially within hospital settings. Detailed case reports including symptoms, viral load and infectiousness, defined by the presence of replication-competent viruses in culture, provide an opportunity to examine the relationship between clinical course, burden and contagiousness, and provide guidance on release from isolation. Objectives We performed a systematic review to investigate the relationship in transplant recipients between serial SARS-CoV-2 RT-PCR cycle threshold (Ct) value or cycle of quantification value (Cq), or other measures of viral burden and the likelihood and duration of the presence of infectious virus based on viral culture including the influence of age, sex, underlying pathologies, degree of immunosuppression, and/or vaccination on this relationship. Methods We searched LitCovid, medRxiv, Google Scholar and WHO Covid-19 databases, from 1 November 2019 until 31 December 2021. We included studies reporting relevant data for transplantees with SARS-CoV-2 infection: results from serial RT-PCR testing and viral culture data from the same respiratory samples. We assessed methodological quality using five criteria, and synthesised the data narratively and graphically. Results We included 10 case reports and case series reporting on 38 transplantees. We observed a relationship between proxies of viral burden and likelihood of shedding replication-competent SARS-CoV-2. Two individuals shed replication-competent viruses over 100 days after infection onset. Lack of standardisation of testing and reporting platforms precludes establishing a definitive viral burden cut-off. However, most transplantees stopped shedding competent viruses when the RT-PCR cycle threshold was above 30 despite differences across platforms. Conclusions Viral burden is a reasonable proxy for infectivity when considered within the context of the clinical status of each patient. Standardised study design and reporting are essential to standardise guidance based on an increasing evidence base.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".