Frontliners on the Move: A Quantitative Analysis of the Prevalence of COVID-19 Reinfection Among Healthcare Workers
Bibliographic record
Abstract
This study was conducted to review relevant articles and demonstrate the prevalence of coronavirus disease 2019 (COVID-19) reinfection among healthcare workers (HCWs). A systemic search was conducted on PubMed and Medline from their inception to July 17, 2021. All statistical analyses were conducted using ReviewManager 5.4.1. Studies meeting the following inclusion criteria were selected: (a) articles having HCWs with COVID-19; (b) studies describing reinfection of COVID-19; and (c) articles having a defined number of patients and controls. Three studies were selected for meta-analysis. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the cohort studies. NOS scores of 1-5 were considered high risk for bias, scores of 6-7 were deemed moderate, and scores >7 were considered low risk for bias. A random-effect model was used when heterogeneity was seen to pool the studies, and the results were reported in inverse variance (IV) and corresponding 95% confidence interval (CI). Pooled prevalence of reinfection of COVID-19 in HCWs was 3% (OR: 0.03 [-0.04, 0.01]; p=0.44; I2=4%). A non-significant prevalence was found among the healthcare professionals in terms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection in Europe. The preformed antibodies were protective against reinfection. However, the waning of antibodies with respect to time was evident, varying differently in different individuals, thereby resulting in reinfection.
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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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 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".