Effect of COVID-19 vaccines for the treatment of people with post-COVID-19 condition: a rapid review
Bibliographic record
Abstract
<ns3:p><ns3:bold>Background:</ns3:bold> Vaccination for coronavirus disease 2019 (COVID-19) has demonstrated reduced risk of hospitalisation and death against more recent variants of COVID-19. Some studies suggested improvements in patients with post-COVID-19 condition (PCC) following vaccination. We systematically explored available evidence on the effect of COVID-19 vaccines on the treatment of people with PCC. <ns3:bold>Methods:</ns3:bold> We conducted a rapid review of the literature following systematic approaches. We searched Medline (OVID), EMBASE (Elsevier), ClinicalTrials.gov, and the International Clinical Trials Registry Platform (ICTRP) for randomised trials, non-randomised trials, controlled before-after studies, and interrupted time-series studies of the effect of COVID-19 vaccines for treating people with PCC. Two independent review authors screened citations. Two review authors extracted data independently. We had planned to assess the risk of bias and use the GRADE approach (Grading of Recommendations, Assessment, Development, and Evaluation) to assess the certainty of evidence if there were completed studies. <ns3:bold>Results:</ns3:bold> We identified two ongoing randomised controlled trials. Both trials examine the effectiveness of therapeutic vaccines on PCC. The anticipated completion date of the CIMAvax-EGFA trial is January 2023, and the completion date of the COVID-19 mRNA vaccine trial is not stated. <ns3:bold>Conclusions:</ns3:bold> There is currently an absence of high‐quality evidence evaluating the effectiveness of COVID-19 vaccines for treating people with post-COVID-19 condition. The absence of published studies and only two ongoing trials highlight the need for additional studies on the effectiveness of vaccines for PCC. We recommend that researchers consider PCC as per the definition provided by the World Health Organization and use the available core outcome set for PCC in deciding which outcomes to measure and report in the trials. <ns3:bold>PROPSERO registration:</ns3:bold> CRD42022330821 (20/06/2022)</ns3:p>
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How this classification was reachedexpand
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".