Screening for SARS-CoV-2 IgM and IgG antibodies among healthcare workers: A single-center study in Egypt
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) pandemic has become a global public health disaster, spreading throughout the world. In order to accurately determine the extent of the pandemic, it is important to accurately identify the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among healthcare workers (HCWs). This study intended to determine the prevalence of SARS-CoV-2 infection among HCWs and examine its correlation with the demographic characteristics of the study participants prior to the implementation of the vaccination campaign. In this cross-sectional study included 431 HCWs from Suez Canal University Hospital in Ismailia, Egypt. Their sera were screened for SARS-CoV-2 antibodies using a one-step novel coronavirus (COVID-19) IgM/IgG antibody test from Artron, Canada. Positive cases were then confirmed using nasal swab real-time reverse transcriptase PCR from Viasure, Spain. Of the 431 study participants, 254 (58.9%) were males and 177 (41.1%) females. The majority of participants, 262 (60.8%), were younger than 30 years old, 150 (34.8%) between 30 and 40 years old, and only 19 (4.4%) older than 40 years old. Out of the total samples, 26 (6%) tested positive for SARS-CoV-2 IgM, while 19 (4.4%) tested positive for both IgM and IgG. The majority of the samples, 386 (89.6%), tested negative for both IgG and IgM. There was no association between the prevalence of SARS-CoV-2 and either sex or age of study participants. In conclusion, during the study period, the prevalence of SARS-CoV-2 infection among healthcare workers at Suez Canal University Hospital in Egypt was relatively low. Additionally, there was no significant correlation observed between the prevalence of positive cases and either age or sex.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".